diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..e3ffc7acc0497f14b52dc668fd087bfc0640597b Binary files /dev/null and b/.DS_Store differ diff --git a/.env b/.env new file mode 100644 index 0000000000000000000000000000000000000000..39108e8ece875e896e1622ee5cfa93fdd96ff68e --- /dev/null +++ b/.env @@ -0,0 +1 @@ +OPENAI_API_KEY = \ No newline at end of file diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..17d8c3094cc69dbd55cae956265186552e50a2b3 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +TTS-Spaces-Arena-25-Dec-2024.png filter=lfs diff=lfs merge=lfs -text +HEARME.wav filter=lfs diff=lfs merge=lfs -text +demo/af_sky.wav filter=lfs diff=lfs merge=lfs -text +output.wav filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3966c3b7ef90c984505796a5c2c2a83a82f970ad --- /dev/null +++ b/README.md @@ -0,0 +1,196 @@ +# **Realtime TTS System** +This repository contains the complete codebase for building your personal Realtime Text-to-Speech (TTS) solution. It integrates a powerful TTS model, gRPC communication, an Express server, and a React-based client. Follow this guide to set up and explore the system effectively. + +--- + +## **Repository Structure** +``` +├── backend/ # Express server for handling API requests +├── frontend/ # React client for user interaction +├── .env # Environment variables (OpenAI API key, etc.) +├── voices # all available voices +├── demo # demo files of model +├── other... +``` + +--- + +## **Setup Guide** + +### **Step 1: Clone the Repository** +Clone this repository to your local machine: +```bash +git clone https://huggingface.co/anuragsingh922/realtime-tts +cd realtime-tts +``` + +--- + +### **Step 2: Python Virtual Environment Setup** +Create a virtual environment to manage dependencies: + +#### macOS/Linux: +```bash +python3 -m venv venv +source venv/bin/activate +``` + +#### Windows: +```bash +python -m venv venv +venv\Scripts\activate +``` + +--- + +### **Step 3: Install Python Dependencies** +With the virtual environment activated, install the required dependencies: +```bash +pip install --upgrade pip setuptools wheel +pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu +pip install -r requirements.txt +``` + +### **Installing eSpeak** +`eSpeak` is a necessary dependency for the TTS system. Follow the instructions below to install it on your platform: + +#### **Ubuntu/Linux** +Use the `apt-get` package manager to install `eSpeak`: +```bash +sudo apt-get update +sudo apt-get install espeak +``` + +#### **macOS** +Install `eSpeak` using [Homebrew](https://brew.sh/): +1. Ensure Homebrew is installed on your system: + ```bash + /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" + ``` +2. Install `espeak`: + ```bash + brew install espeak + ``` + +#### **Windows** +For Windows, follow these steps to install `eSpeak`: +1. Download the eSpeak installer from the official website: [eSpeak Downloads](http://espeak.sourceforge.net/download.html). +2. Run the installer and follow the on-screen instructions to complete the installation. +3. Add the `eSpeak` installation path to your system's `PATH` environment variable: + - Open **System Properties** → **Advanced** → **Environment Variables**. + - In the "System Variables" section, find the `Path` variable and edit it. + - Add the path to the `espeak.exe` file (e.g., `C:\Program Files (x86)\eSpeak`). +4. Verify the installation: + Open Command Prompt and run: + ```cmd + espeak --version + ``` + +--- + +### **Verification** +After installing `eSpeak`, verify it is correctly set up by running: +```bash +espeak "Hello, world!" +``` + +This should output "Hello, world!" as audio on your system. + +--- + +### **Step 4: Backend Setup (Express Server)** +1. Navigate to the `backend` directory: + ```bash + cd backend + ``` +2. Install Node.js dependencies: + ```bash + npm install + ``` +3. Update the `config.env` file with your Deepgram API key: + - Open `config.env` in a text editor. + - Replace `` with your actual Deepgram API key. + +4. Start the Express server: + ```bash + node app.js + ``` + +--- + +### **Step 5: Frontend Setup (React Client)** +1. Open a new terminal and navigate to the `frontend` directory: + ```bash + cd frontend + ``` +2. Install client dependencies: + ```bash + npm install + ``` +3. Start the client: + ```bash + npm start + ``` + +--- + +### **Step 6: Start the TTS Server** +1. Add your OpenAI API key to the `.env` file: + - Open `.env` in a text editor. + - Replace `` with your actual OpenAI API key. + +2. Start the TTS server: + ```bash + python3 app.py + ``` + +--- + +### **Step 7: Test the Full System** +- Once all servers are running: + 1. Access the React client at [http://localhost:3000](http://localhost:3000). + 2. Interact with the TTS system via the web interface. + +--- + +## **Model Used** +This project utilizes the [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) TTS model hosted on Hugging Face. The model generates high-quality, realtime text-to-speech outputs. + +--- + +## **Key Features** +1. **Realtime TTS Generation**: Convert text input into speech with minimal latency. +2. **React Client**: A user-friendly frontend for interaction. +3. **Express Backend**: Handles API requests and integrates the TTS system with external services. +4. **gRPC Communication**: Seamless communication between the TTS server and other components. +5. **Configurable APIs**: Supports OpenAI and Deepgram API integrations. + +--- + +## **Dependencies** + +### Python: +- `torch`, `torchvision`, `torchaudio` +- `phonemizer` +- `transformers` +- `scipy` +- `munch` +- `python-dotenv` +- `openai` +- `grpcio`, `grpcio-tools` +- `espeak` + +### Node.js: +- Express server dependencies (`npm install` in `backend`). +- React client dependencies (`npm install` in `frontend`). + +--- + +## **Contributing** +Contributions are welcome! Feel free to fork this repository and create a pull request with your improvements. + +--- + +## **Acknowledgments** +- [Hugging Face](https://huggingface.co/) for hosting the Kokoro-82M model. +- The amazing communities behind PyTorch, OpenAI, and Deepgram APIs. \ No newline at end of file diff --git a/__pycache__/chat_database.cpython-310.pyc b/__pycache__/chat_database.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7bc0136fec4560b8f310fb9c6751f81330216624 Binary files /dev/null and b/__pycache__/chat_database.cpython-310.pyc differ diff --git a/__pycache__/chat_database.cpython-313.pyc b/__pycache__/chat_database.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3136cb4424c88b9b9052182d622993dab002cf50 Binary files /dev/null and b/__pycache__/chat_database.cpython-313.pyc differ diff --git a/__pycache__/grpc.cpython-310.pyc b/__pycache__/grpc.cpython-310.pyc new file mode 100644 index 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+import os +from chat_database import save_chat_entry, get_chat_history + +load_dotenv() + +# Device configuration +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +# Load the Kokoro model +MODEL = build_model('kokoro-v0_19.pth', device) + +# Specify the voice name and load the voice pack +VOICE_NAME = [ + 'af', + 'af_bella', 'af_sarah', 'am_adam', 'am_michael', + 'bf_emma', 'bf_isabella', 'bm_george', 'bm_lewis', + 'af_nicole', 'af_sky', +][0] +VOICEPACK = torch.load(f'voices/{VOICE_NAME}.pt', weights_only=True).to(device) + + +client = OpenAI( + api_key= os.getenv("OPENAI_API_KEY") +) + +def chunk_text(text, max_chars=2040): + sentences = re.split(r'(?<=[.!?])\s+', text) + chunks = [] + current_chunk = [] + current_length = 0 + for sentence in sentences: + sentence_length = len(sentence) + if current_length + sentence_length <= max_chars: + current_chunk.append(sentence) + current_length += sentence_length + else: + if current_chunk: + chunks.append(' '.join(current_chunk)) + current_chunk = [sentence] + current_length = sentence_length + if current_chunk: + chunks.append(' '.join(current_chunk)) + return chunks + +def generate_audio_from_chunks(text, model, voicepack, voice_name): + chunks = chunk_text(text) + combined_audio = np.array([]) + for chunk in chunks: + try: + audio, _ = generate(model, chunk, voicepack, lang=voice_name[0]) + combined_audio = np.concatenate([combined_audio, audio]) if combined_audio.size > 0 else audio + except Exception: + pass + return combined_audio + +def save_audio_to_file(audio_data, file_number, sample_rate=24000): + filename = f"output-{file_number}.wav" + with wave.open(filename, 'wb') as wav_file: + wav_file.setnchannels(1) + wav_file.setsampwidth(2) + wav_file.setframerate(sample_rate) + audio_int16 = (audio_data * 32767).astype(np.int16) + wav_file.writeframes(audio_int16.tobytes()) + return filename + +def getResponse(text , session_id): + try: + chat_history = get_chat_history(session_id) + response = client.chat.completions.create( + model='gpt-3.5-turbo', + messages=chat_history, + stream=True + ) + return response + except Exception as e: + print("Error in getResponse : " , e) + +def get_audio_bytes(audio_data, sample_rate=24000): + wav_bytes = io.BytesIO() + with wave.open(wav_bytes, 'wb') as wav_file: + wav_file.setnchannels(1) + wav_file.setsampwidth(2) + wav_file.setframerate(sample_rate) + audio_int16 = (audio_data * 32767).astype(np.int16) + wav_file.writeframes(audio_int16.tobytes()) + wav_bytes.seek(0) + return wav_bytes.read() + +def dummy_bytes(): + buffer = io.BytesIO() + dummy_data = b"This is a test of dummy byte data." + buffer.write(dummy_data) + buffer.seek(0) + byte_value = buffer.getvalue() + return byte_value + + +class TextToSpeechServicer(text_to_speech_pb2_grpc.TextToSpeechServiceServicer): + def ProcessText(self, request_iterator, context): + try: + print("Received new request") + parameters = { + "processing_active": False, + "queue": deque(), + "file_number": 0, + "session_id": "", + "interrupt_seq" : 0 + } + for request in request_iterator: + field = request.WhichOneof('request_data') + if field == 'metadata': + parameters["session_id"] = request.metadata.session_id + continue + elif field == 'text': + text = request.text + if not text: + continue + save_chat_entry(parameters["session_id"] , "user" , text) + parameters["queue"].clear() + yield text_to_speech_pb2.ProcessTextResponse( + buffer = dummy_bytes(), + session_id=parameters["session_id"], + sequence_id = "-2", + transcript=text, + ) + final_response = "" + response = getResponse(text , parameters["session_id"]) + for chunk in response: + msg = chunk.choices[0].delta.content + if msg: + final_response += msg + if final_response.endswith(('.', '!', '?')): + parameters["file_number"] += 1 + parameters["queue"].append((final_response, parameters["file_number"])) + final_response = "" + if not parameters["processing_active"]: + yield from self.process_queue(parameters) + + if final_response: + parameters["file_number"] += 1 + parameters["queue"].append((final_response, parameters["file_number"])) + if not parameters["processing_active"]: + yield from self.process_queue(parameters) + + elif field == 'status': + transcript = request.status.transcript + played_seq = request.status.played_seq + interrupt_seq = request.status.interrupt_seq + parameters["interrupt_seq"] = interrupt_seq + save_chat_entry(parameters["session_id"] , "assistant" , transcript) + continue + else: + continue + except Exception as e: + print("Error in ProcessText:", e) + + def process_queue(self , parameters): + try: + while True: + if not parameters["queue"]: + parameters["processing_active"] = False + break + parameters["processing_active"] = True + sentence, file_number = parameters["queue"].popleft() + if file_number <= int(parameters["interrupt_seq"]): + continue + combined_audio = generate_audio_from_chunks(sentence, MODEL, VOICEPACK, VOICE_NAME) + audio_bytes = get_audio_bytes(combined_audio) + # filename = save_audio_to_file(combined_audio, file_number) + yield text_to_speech_pb2.ProcessTextResponse( + buffer=audio_bytes, + session_id=parameters["session_id"], + sequence_id=str(file_number), + transcript=sentence, + ) + except Exception as e: + parameters["processing_active"] = False + print("Error in process_queue:", e) + + +def serve(): + print("Starting gRPC server...") + server = grpc.server(futures.ThreadPoolExecutor(max_workers=1)) + text_to_speech_pb2_grpc.add_TextToSpeechServiceServicer_to_server(TextToSpeechServicer(), server) + server.add_insecure_port('[::]:8081') + server.start() + print("gRPC server is running on port 8081") + server.wait_for_termination() + + +if __name__ == "__main__": + serve() diff --git a/backend/.DS_Store b/backend/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..30836c674aadab712556baeb913704fb7368fa14 Binary files /dev/null and b/backend/.DS_Store differ diff --git a/backend/.gitignore b/backend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..90157b1bc545f1d9cc32acfc8b89067eba07ce61 --- /dev/null +++ b/backend/.gitignore @@ -0,0 +1,2 @@ +/node_modules +.DS_Store \ No newline at end of file diff --git a/backend/app.js b/backend/app.js new file mode 100644 index 0000000000000000000000000000000000000000..4512a84393381a6efad3cd9f29adeca82349a38b --- /dev/null +++ b/backend/app.js @@ -0,0 +1,22 @@ +const express = require("express"); +const cors = require("cors"); +const bodyParser = require("body-parser"); +const app = express(); +require("express-ws")(app); +app.use(express.json()); +app.use(cors()); +app.use(express.urlencoded({ extended: true })); +app.use(bodyParser.json()); +const port = 8080; + +const { audio_stream } = require("./handle-realtime-tts/sttModelSocket.js"); + +app.get("/health", (req, res) => { + res.send("Green"); +}); + +app.ws("/v2v", audio_stream); + +app.listen(port, () => { + console.log(`Example app listening at http://localhost:${port}`); +}); diff --git a/backend/config.env b/backend/config.env new file mode 100644 index 0000000000000000000000000000000000000000..065a40289c8fbfb9edf58180e31a4f609c46fba1 --- /dev/null +++ b/backend/config.env @@ -0,0 +1 @@ +DEEPGRAM_KEY = \ No newline at end of file diff --git a/backend/config.js b/backend/config.js new file mode 100644 index 0000000000000000000000000000000000000000..e5f4d7a078e957cf017e4f47e9c96cc721c57453 --- /dev/null +++ b/backend/config.js @@ -0,0 +1,7 @@ +require("dotenv").config({ path: "./config.env" }); + +const deepgram_key = process.env.DEEPGRAM_KEY; + +module.exports = { + deepgram_key +}; diff --git a/backend/handle-realtime-tts/cleangRPCconnections.js b/backend/handle-realtime-tts/cleangRPCconnections.js new file mode 100644 index 0000000000000000000000000000000000000000..fe26174af471ef647d3fcae08a7e86c020531eae --- /dev/null +++ b/backend/handle-realtime-tts/cleangRPCconnections.js @@ -0,0 +1,47 @@ +const cleanupConnection = async (session) => { + try { + if (session.channel) { + const state = session.channel.getConnectivityState(false); + console.log(`Client : ${state}`); + if (state !== 4) { + console.log("Closing call and client."); + session.client.close(); + session.call.end(); + session.client = null; + session.call = null; + } + } else { + try { + if (session.client) { + session.client.close(); + if (session.call) { + session.call.end(); + } + session.call = null; + session.client = null; + session.channel = null; + } + } catch (err) { + session.call = null; + session.client = null; + session.channel = null; + } + } + console.log("gRPC connection ended."); + } catch (err) { + if (session.call) { + session.call.end(); + } + session.call = null; + console.log("Error ending gRPC connection: ", err); + } finally { + if (session.call) { + session.call.end(); + } + session.call = null; + session.client = null; + session.channel = null; + } +}; + +module.exports = { cleanupConnection }; diff --git a/backend/handle-realtime-tts/makegRPCconnection.js b/backend/handle-realtime-tts/makegRPCconnection.js new file mode 100644 index 0000000000000000000000000000000000000000..564b190e1906035fdc81d1bcf23efb8bacc308b2 --- /dev/null +++ b/backend/handle-realtime-tts/makegRPCconnection.js @@ -0,0 +1,40 @@ +const grpc = require("@grpc/grpc-js"); +const protoLoader = require("@grpc/proto-loader"); +const path = require("path"); + +const getgRPCConnection = (session) => { + return new Promise((resolve, reject) => { + protoLoader + .load(path.join(__dirname, "text_to_speech.proto"), { + keepCase: true, + longs: String, + enums: String, + defaults: true, + oneofs: true, + }) + .then((packageDefinition) => { + const textToSpeechProto = grpc.loadPackageDefinition(packageDefinition).texttospeech; + const client = new textToSpeechProto.TextToSpeechService( + "localhost:8081", + grpc.credentials.createInsecure() + ); + session.client = client; + const channel = session.client.getChannel(); + session.channel = channel; + console.log("Made connection"); + session.client = client; + + + const call = client.ProcessText(); + resolve(call); + + }) + .catch((error) => { + session.client = null; + console.error("Error loading proto file:", error); + reject(new Error("Error in making gRPC Connection.")); + }); + }); +}; + +module.exports = { getgRPCConnection }; \ No newline at end of file diff --git a/backend/handle-realtime-tts/sttModelSocket.js b/backend/handle-realtime-tts/sttModelSocket.js new file mode 100644 index 0000000000000000000000000000000000000000..6321691ba641a2a8ee905be1cd03a6188561df1a --- /dev/null +++ b/backend/handle-realtime-tts/sttModelSocket.js @@ -0,0 +1,289 @@ +const isBuffer = require("is-buffer"); +const { Buffer } = require("buffer"); +const {deepgram_key} = require("../config"); +const Session = require("../utils/session.js"); +const { cleanupConnection } = require("./cleangRPCconnections.js"); +const { getgRPCConnection } = require("./makegRPCconnection.js"); +const { updateChathistory } = require("../providers/updateChathistory.js"); +const { createClient, LiveTranscriptionEvents } = require("@deepgram/sdk"); +const deepgram = createClient(deepgram_key); + +const audio_stream = async (wss, req) => { + try { + const session = new Session(); + + wss.send(JSON.stringify({ type: "initial", msg: "connected" })); + + + const connection = deepgram.listen.live({ + punctuate: true, + interim_results: true, + speech_final: true, + encoding: "linear16", + sample_rate: 16000, + model: "nova-2", + speech_final: true, + version: "latest", + }); + + const callMLServer = async (text) => { + try { + session.call.write({ text: text }); + } catch (error) { + console.error("Error in calling ml server : ", error); + } + } + + + connection.on(LiveTranscriptionEvents.Open, () => { + console.log(LiveTranscriptionEvents.Open); + connection.on(LiveTranscriptionEvents.Close, () => { + console.log("Connection closed."); + }); + + connection.on(LiveTranscriptionEvents.Transcript, (data) => { + const text = data?.channel?.alternatives[0]?.transcript; + // console.log("Response : ", text); + if (data.is_final && data.speech_final && text) { + console.log("Response : ", text); + callMLServer(text); + } + }); + + connection.on(LiveTranscriptionEvents.Metadata, (data) => { + console.log(data); + }); + + connection.on(LiveTranscriptionEvents.Error, (err) => { + console.error(err); + }); + }); + + + wss.on("message", async (message) => { + try { + if (isBuffer(message) && session.call) { + try { + const audioChunk = { + audio_data: message, + }; + + try { + if (connection && connection.getReadyState() == 1) { + connection.send(message); + } + } catch (error) { + console.log("Error sending buffer to deepgram : ", error); + } + } catch (err) { + console.error("Error writing to stream: ", err); + } + } + + // Handle message not of typeof buffer + if (typeof message === "string") { + try { + const data = JSON.parse(message); + + const { type, msg } = data; + + switch (type) { + case "start": + session.starttime = Date.now(); + session.chathistory = []; + session.chathistorybackup = []; + console.log("Making Connection with gRPC..."); + try { + console.time("grpcconnection"); + session.call = await getgRPCConnection(session); + console.timeEnd("grpcconnection"); + const state = session.channel.getConnectivityState(false); + console.log(`Client : ${state}`); + session.saved = false; + wss.send(JSON.stringify({ type: "ready", msg: "connected" })); + console.log("Connected to gRPC."); + + const { + sessionId, + } = JSON.parse(msg); + const metadata = { + metadata: { + session_id: sessionId, + }, + }; + if (session.call) { + console.log("Sending metadata.") + session.call.write(metadata); + } + } catch (err) { + await cleanupConnection(session); + console.error("Error in making gRPC Connection. : ", err); + } + session.call.on("data", (response) => { + console.log("Data : ", response); + + const {session_id , sequence_id , transcript , buffer} = response; + + const metadata = JSON.stringify({ + session_id: session_id, + sequence_id: sequence_id, + transcript: transcript, + }); + + if (sequence_id === "-2") { + session.latency = Date.now(); + wss.send(JSON.stringify({ type: "clear", msg: "clear" })); + session.chathistory = [...session.chathistorybackup]; + wss.send( + JSON.stringify({ + type: "chathistory", + msg: session.chathistorybackup, + }) + ); + const wavBuffer = Buffer.concat([ + Buffer.from(metadata), + Buffer.from([0]), + buffer, + ]); + + const base64buffer = wavBuffer.toString("base64"); + wss.send( + JSON.stringify({ type: "media", msg: base64buffer }) + ); + session.chathistory.push({ + speaker: "USER", + content: transcript, + }); + wss.send( + JSON.stringify({ + type: "chathistory", + msg: session.chathistory, + }) + ); + session.chathistorybackup.push({ + speaker: "USER", + content: transcript, + }); + return; + } + + if (sequence_id === "0") { + wss.send(JSON.stringify({ type: "pause", msg: "pause" })); + session.cansend = false; + return; + } + + if (sequence_id === "-1") { + wss.send( + JSON.stringify({ type: "continue", msg: "continue" }) + ); + return; + } + + if (sequence_id === "1") { + const latency = Date.now() - session.latency; + console.log("First Response Latency: ", latency, "ms"); + session.latency = 0; + // wss.send(JSON.stringify({ type: "clear", msg: "clear" })); + session.cansend = true; + } + + if (!buffer) { + return; + } + + if (!session.cansend && sequence_id !== "0") { + return; + } + + // Combine header and PCM data into a single Buffer + const wavBuffer = Buffer.concat([ + Buffer.from(metadata), + Buffer.from([0]), + buffer, + ]); + + const base64buffer = wavBuffer.toString("base64"); + wss.send( + JSON.stringify({ type: "media", msg: base64buffer }) + ); + + updateChathistory(transcript, false, session); + + wss.send( + JSON.stringify({ + type: "chathistory", + msg: session.chathistory, + }) + ); + }); + + session.call.on("end", async () => { + console.log("Ended"); + await cleanupConnection(session); + try { + wss.send(JSON.stringify({ type: "end", msg: "end" })); + } catch (err) { } + console.log("Stream ended"); + }); + + session.call.on("error", async (error) => { + console.error(`Stream error: ${error}`); + try { + wss.send(JSON.stringify({ type: "end", msg: "end" })); + } catch (err) { } + await cleanupConnection(session); + }); + break; + + case "status": + const { session_id, sequence_id, transcript } = msg; + const status = { + status: { + transcript : transcript, + played_seq: sequence_id, + interrupt_seq: sequence_id, + }, + }; + + if (session.call) { + session.call.write(status); + } + + updateChathistory(transcript, true, session); + break; + + case "stop": + console.log("Client Stoped the stream."); + await cleanupConnection(session); + break; + default: + console.log("Type not handled."); + } + } catch (err) { + console.log(`Not a valid json : ${err}`); + } + } + } catch (err) { + console.error(`Error in wss.onmessage : ${err}`); + } + }); + + wss.on("close", async () => { + await cleanupConnection(session); + console.log("WebSocket connection closed."); + }); + + wss.on("error", async (err) => { + console.error(`WebSocket error: ${err}`); + await cleanupConnection(session); + }); + } catch (err) { + try { + console.log(err) + wss.send(JSON.stringify({ type: "end", msg: "end" })); + } catch (err) { } + } +}; + +module.exports = { audio_stream }; diff --git a/backend/handle-realtime-tts/text_to_speech.proto b/backend/handle-realtime-tts/text_to_speech.proto new file mode 100644 index 0000000000000000000000000000000000000000..e9c2b94bd00b9520192f5eace77ac29970f7ee5e --- /dev/null +++ b/backend/handle-realtime-tts/text_to_speech.proto @@ -0,0 +1,32 @@ +syntax = "proto3"; + +package texttospeech; + +service TextToSpeechService { + rpc ProcessText (stream ProcessTextRequest) returns (stream ProcessTextResponse); +} + +message ProcessTextRequest { + 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"^21.1.1" + }, + "engines": { + "node": ">=12" + } + }, + "node_modules/yargs-parser": { + "version": "21.1.1", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz", + "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==", + "engines": { + "node": ">=12" + } + } + } +} diff --git a/backend/package.json b/backend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..73ec70cc30c31af150a2a4cfa2e57b9183c45fb8 --- /dev/null +++ b/backend/package.json @@ -0,0 +1,27 @@ +{ + "name": "backend", + "version": "1.0.0", + "description": "", + "main": "index.js", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "keywords": [], + "author": "", + "license": "ISC", + "dependencies": { + "@deepgram/sdk": "^3.9.0", + "@geckos.io/server": "^3.0.0", + "@grpc/grpc-js": "^1.11.3", + "axios": "^1.7.9", + "bcryptjs": "^2.4.3", + "cors": "^2.8.5", + "crypto": "^1.0.1", + "dotenv": "^16.4.5", + "express": "^4.21.0", + "express-ws": "^5.0.2", + "is-buffer": "^2.0.5", + "jsonwebtoken": "^9.0.2", + "module": "^1.2.5" + } +} diff --git a/backend/providers/updateChathistory.js b/backend/providers/updateChathistory.js new file mode 100644 index 0000000000000000000000000000000000000000..df99d4cf0a9ae406b49ef7fbd6e9fdd76a3eea36 --- /dev/null +++ b/backend/providers/updateChathistory.js @@ -0,0 +1,46 @@ +const updateChathistory = (transcript, backup , session) => { + try { + if (backup) { + if ( + session.chathistorybackup.length > 0 && + session.chathistorybackup[session.chathistorybackup.length - 1] + .speaker === "USER" + ) { + session.chathistorybackup.push({ + speaker: "AI", + content: ``, + }); + } + if ( + session.chathistory && + session.chathistory.length > 0 && + session.chathistory[session.chathistory.length - 1].speaker === "AI" + ) { + session.chathistorybackup[ + session.chathistorybackup.length - 1 + ].content += ` ${transcript}`; + } + } else if (!backup) { + if ( + session.chathistory.length > 0 && + session.chathistory[session.chathistory.length - 1].speaker === "USER" + ) { + session.chathistory.push({ speaker: "AI", content: `` }); + } + if ( + session.chathistory && + session.chathistory.length > 0 && + session.chathistory[session.chathistory.length - 1].speaker === "AI" + ) { + session.chathistory[ + session.chathistory.length - 1 + ].content += ` ${transcript}`; + } + } + } catch (error) { + console.log("Error in updating chathistory : ", error); + } +}; + + +module.exports = {updateChathistory} \ No newline at end of file diff --git a/backend/utils/session.js b/backend/utils/session.js new file mode 100644 index 0000000000000000000000000000000000000000..2d2a3ecc78aca5db46b21e3104546482c3d7e48e --- /dev/null +++ b/backend/utils/session.js @@ -0,0 +1,15 @@ +class Session { + constructor() { + this.call = null; + this.client = null; + this.channel = null; + this.saved = false; + this.starttime = Date.now(); + this.cansend = false; + this.chathistory = []; + this.chathistorybackup = []; + this.latency = 0; + } +} + +module.exports = Session; diff --git a/chat_database.py b/chat_database.py new file mode 100644 index 0000000000000000000000000000000000000000..89806c103ae50c804e33ba04ed0e1ce739a9e342 --- /dev/null +++ b/chat_database.py @@ -0,0 +1,64 @@ +import pickle +import os + +FILE_PATH = "chat_history.pkl" + +if not os.path.exists(FILE_PATH): + with open(FILE_PATH, "wb") as file: + pickle.dump({}, file) + +def save_chat_entry(session_id, role, transcript): + try: + with open(FILE_PATH, "rb") as file: + data = pickle.load(file) + + if session_id not in data: + data[session_id] = [] + + if role == "user": + data[session_id].append({ + "role": role, + "transcript": transcript + }) + else: + if data[session_id] and data[session_id][-1]['role'] == "assistant": + data[session_id][-1]['transcript'] += " " + transcript + else: + data[session_id].append({ + "role": role, + "transcript": transcript + }) + + with open(FILE_PATH, "wb") as file: + pickle.dump(data, file) + + except Exception as e: + print(f"Error saving chat entry: {e}") + + +def get_chat_history(session_id): + try: + with open(FILE_PATH, "rb") as file: + data = pickle.load(file) + + chat_history = data.get(session_id, []) + + if not chat_history: + return [] + + message_history = [] + for entry in chat_history: + role = entry.get('role', '') + transcript = entry.get('transcript', '') + if role and transcript: + message_history.append({"role": role, "content": transcript}) + + return message_history + + except (FileNotFoundError, pickle.UnpicklingError) as e: + print(f"Error reading or parsing the file: {e}") + return [] + except Exception as e: + print(f"Unexpected error: {e}") + return [] + diff --git a/chat_history.pkl b/chat_history.pkl new file mode 100644 index 0000000000000000000000000000000000000000..28bef55002a6ae3018ed1515f2f4506a23a9b079 --- /dev/null +++ b/chat_history.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d229c43b598eacb4620b5bf033308ff27c1e9979af506afd1e58d7e6ba24c9da +size 12508 diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..29e12f5e6f19d8b27dcdb2cd37e8b12fd89590c5 --- /dev/null +++ b/config.json @@ -0,0 +1,26 @@ +{ + "decoder": { + "type": "istftnet", + "upsample_kernel_sizes": [20, 12], + "upsample_rates": [10, 6], + "gen_istft_hop_size": 5, + "gen_istft_n_fft": 20, + "resblock_dilation_sizes": [ + [1, 3, 5], + [1, 3, 5], + [1, 3, 5] + ], + "resblock_kernel_sizes": [3, 7, 11], + "upsample_initial_channel": 512 + }, + "dim_in": 64, + "dropout": 0.2, + "hidden_dim": 512, + "max_conv_dim": 512, + "max_dur": 50, + "multispeaker": true, + "n_layer": 3, + "n_mels": 80, + "n_token": 178, + "style_dim": 128 +} \ No newline at end of file diff --git a/demo/HEARME.txt b/demo/HEARME.txt new file mode 100644 index 0000000000000000000000000000000000000000..d460cf4da13952dac7796e7f879e7d9328cb36e6 --- /dev/null +++ b/demo/HEARME.txt @@ -0,0 +1,47 @@ +Kokoro is a frontier TTS model for its size of 82 million parameters. + +On the 25th of December, 2024, Kokoro v0 point 19 weights were permissively released in full fp32 precision along with 2 voicepacks (Bella and Sarah), all under an Apache 2 license. + +At the time of release, Kokoro v0 point 19 was the number 1 ranked model in TTS Spaces Arena. With 82 million parameters trained for under 20 epics on under 100 total hours of audio, Kokoro achieved higher Eelo in this single-voice Arena setting, over larger models. Kokoro's ability to top this Eelo ladder using relatively low compute and data, suggests that the scaling law for traditional TTS models might have a steeper slope than previously expected. + +Licenses. Apache 2 weights in this repository. MIT inference code. GPLv3 dependency in espeak NG. + +The inference code was originally MIT licensed by the paper author. Note that this card applies only to this model, Kokoro. + +Evaluation. Metric: Eelo rating. Leaderboard: TTS Spaces Arena. + +The voice ranked in the Arena is a 50 50 mix of Bella and Sarah. For your convenience, this mix is included in this repository as A-F dot PT, but you can trivially re-produce it. + +Training Details. + +Compute: Kokoro was trained on "A100 80GB v-ram instances" rented from Vast.ai. Vast was chosen over other compute providers due to its competitive on-demand hourly rates. The average hourly cost for the A100 80GB v-ram instances used for training was below $1 per hour per GPU, which was around half the quoted rates from other providers at the time. + +Data: Kokoro was trained exclusively on permissive non-copyrighted audio data and IPA phoneme labels. Examples of permissive non-copyrighted audio include: + +Public domain audio. Audio licensed under Apache, MIT, etc. + +Synthetic audio[1] generated by closed[2] TTS models from large providers. + +Epics: Less than 20 Epics. Total Dataset Size: Less than 100 hours of audio. + +Limitations. Kokoro v0 point 19 is limited in some ways, in its training set and architecture: + +Lacks voice cloning capability, likely due to small, under 100 hour training set. + +Relies on external g2p, which introduces a class of g2p failure modes. + +Training dataset is mostly long-form reading and narration, not conversation. + +At 82 million parameters, Kokoro almost certainly falls to a well-trained 1B+ parameter diffusion transformer, or a many-billion-parameter M LLM like GPT 4o or Gemini 2 Flash. + +Multilingual capability is architecturally feasible, but training data is almost entirely English. + +Will the other voicepacks be released? + +There is currently no release date scheduled for the other voicepacks, but in the meantime you can try them in the hosted demo. + +Acknowledgements. yL4 5 7 9 for architecting StyleTTS 2. + +Pendrokar for adding Kokoro as a contender in the TTS Spaces Arena. + +Model Card Contact. @rzvzn on Discord. \ No newline at end of file diff --git a/demo/HEARME.wav b/demo/HEARME.wav new file mode 100644 index 0000000000000000000000000000000000000000..7702c63e3933b4c23f3bc3bd45728965b7c607b0 --- /dev/null +++ b/demo/HEARME.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a66b2d1022df066f39773d650ff6c523d635407e88054c02066a821d50246800 +size 3407872 diff --git a/demo/TTS-Spaces-Arena-25-Dec-2024.png b/demo/TTS-Spaces-Arena-25-Dec-2024.png new file mode 100644 index 0000000000000000000000000000000000000000..5832d19cdc9d2ffa8587dab791c7aad8b4f6a91d --- /dev/null +++ b/demo/TTS-Spaces-Arena-25-Dec-2024.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e78b5ec1557323fa0e62681c83f6b81777f9834b91bbf26bf7567b036f011d52 +size 1065265 diff --git a/demo/af_sky.txt b/demo/af_sky.txt new file mode 100644 index 0000000000000000000000000000000000000000..ec7b4a24b18c12a946254bc9c08b1b769933f77d --- /dev/null +++ b/demo/af_sky.txt @@ -0,0 +1,11 @@ +Last September, I received an offer from Sam Altman, who wanted to hire me to voice the current ChatGPT 4 system. He told me that he felt that by my voicing the system, I could bridge the gap between tech companies and creatives and help consumers to feel comfortable with the seismic shift concerning humans and AI. He said he felt that my voice would be comforting to people. + +After much consideration and for personal reasons, I declined the offer. Nine months later, my friends, family and the general public all noted how much the newest system named Sky sounded like me. + +When I heard the released demo, I was shocked, angered and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news ou'tlits could not tell the difference. Mr. Altman even insinuated that the similarity was intentional, tweeting a single word — hur — a reference to the film in which I voiced a chat system, Samantha, who forms an intimate relationship with a human. + +Two days before the ChatGPT 4 demo was released, Mr. Altman contacted my agent, asking me to reconsider. Before we could connect, the system was out there. + +As a result of their actions, I was forced to hire legal counsel, who wrote two letters to Mr. Altman and OpenAI, setting out what they had done and asking them to detail the exact process by which they created the Sky voice. Consequently, OpenAI reluctantly agreed to take down the Sky voice. + +In a time when we are all grappling with deepfakes and the protection of our own likeness, our own work, our own identities, I believe these are questions that deserve absolute clarity. I look forward to resolution in the form of transparency and the passage of appropriate legislation to help ensure that individual rights are protected. \ No newline at end of file diff --git a/demo/af_sky.wav b/demo/af_sky.wav new file mode 100644 index 0000000000000000000000000000000000000000..1afe28ca2eef3a272686f2725b642e899a4d024f --- /dev/null +++ b/demo/af_sky.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96491c978f85727b49b638beb94ad591a3830ce4a657db02d740acd61ec4322c +size 3407872 diff --git a/demo/restoring-sky.md b/demo/restoring-sky.md new file mode 100644 index 0000000000000000000000000000000000000000..8a2fa6375ef7881e0859abac906d442de6cf7415 --- /dev/null +++ b/demo/restoring-sky.md @@ -0,0 +1,42 @@ +# Restoring Sky & reflecting on Kokoro + +kokoro + +For those who don't know, [Kokoro](https://huggingface.co/hexgrad/Kokoro-82M) is an Apache TTS model that uses a skinny version of the open [StyleTTS 2](https://github.com/yl4579/StyleTTS2/tree/main) architecture. + +Based on leaderboard [Elo rating](https://huggingface.co/hexgrad/Kokoro-82M#evaluation) (prior to getting [review bombed](https://huggingface.co/datasets/Pendrokar/TTS_Arena/discussions/2)), Kokoro appears to do more with less, a theme that is surely [top-of-mind](https://huggingface.co/deepseek-ai/DeepSeek-V3) for many. It's peak performance on specific voices is comparable or better than much larger models, but it has not yet been trained on enough data to effectively zero-shot out of distribution (aka voice cloning). + +Tonight on NYE, `af_sky` joins Kokoro's roster of downloadable voices. This follows last night's quiet release of `af_nicole`, and an additional 8 voices are currently available: 2F 2M voices each for American & British English. + +Nicole in particular was trained on ~10 hours of synthetic data, and demonstrates that you _can_ include unique speaking styles in a general-purpose TTS model without affecting the stock voices (even in a low data small model): a good sign for scalability. + +Sky is interesting because it is the voice that ScarJo [got OpenAI to take down](https://x.com/OpenAI/status/1792443575839678909), so new training data cannot be generated. However, OpenAI did not remove 2023 samples of Sky from their [blog post](https://openai.com/index/chatgpt-can-now-see-hear-and-speak/), and along with a few seconds lying around various other parts of the internet, we can cobble together about 3 minutes of 2023 Sky. + +```sh +wget https://cdn.openai.com/new-voice-and-image-capabilities-in-chatgpt/hd/story-sky.mp3 +wget https://cdn.openai.com/new-voice-and-image-capabilities-in-chatgpt/hd/recipe-sky.mp3 +wget https://cdn.openai.com/new-voice-and-image-capabilities-in-chatgpt/hd/speech-sky.mp3 +wget https://cdn.openai.com/new-voice-and-image-capabilities-in-chatgpt/hd/poem-sky.mp3 +wget https://cdn.openai.com/new-voice-and-image-capabilities-in-chatgpt/hd/info-sky.mp3 +``` + +To be clear, this is not the first attempt to reconstruct Sky. On X, Benjamin De Kraker posted: +> Here's the official statement released by Scarlett Johansson, detailing OpenAI's alleged illegal usage of her voice... +> ...read by the Sky AI voice, because irony. +> https://x.com/BenjaminDEKR/status/1792693868497871086 + +and in the replies, he [stated](https://x.com/BenjaminDEKR/status/1792714347275501595): +> It's an ElevenLabs clone I made based on Sky audio before they removed it. Not perfect. + +Here is `Kokoro/af_sky`'s rendition of the same: + + +A crude reconstruction, but the model that produced that voice is Apache FOSS that can be downloaded from HF and run locally. You can reproduce the above by dragging the [text script](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/demo/af_sky.txt) (note a handful of modified chars for better delivery) into the "Long Form" tab of this [hosted demo](https://huggingface.co/spaces/hexgrad/Kokoro-TTS), or you can download the [model weights](https://huggingface.co/hexgrad/Kokoro-82M), install dependencies and DIY. + +Sky shows that it is possible to reconstruct a voice—maybe a shadow of its former self, but a reconstruction nonetheless—from fairly little training data. + +### What's next + +Kokoro is a good start, but I can think of some tricks that might make it better, beginning with better data. More on this in another article. + +Feel free to check out [Kokoro's weights](https://huggingface.co/hexgrad/Kokoro-82M), try out a no-install [hosted demo](https://huggingface.co/spaces/hexgrad/Kokoro-TTS), and/or [join the Discord](https://discord.gg/QuGxSWBfQy). \ No newline at end of file diff --git a/fp16/halve.py b/fp16/halve.py new file mode 100644 index 0000000000000000000000000000000000000000..5baeea857f1efc5f4ddafb5b922447af51c7b29e --- /dev/null +++ b/fp16/halve.py @@ -0,0 +1,17 @@ +from hashlib import sha256 +from pathlib import Path +import torch + +path = Path(__file__).parent.parent / 'kokoro-v0_19.pth' +assert path.exists(), f'No model pth found at {path}' + +net = torch.load(path, map_location='cpu', weights_only=True)['net'] +for a in net: + for b in net[a]: + net[a][b] = net[a][b].half() + +torch.save(dict(net=net), 'kokoro-v0_19-half.pth') +with open('kokoro-v0_19-half.pth', 'rb') as rb: + h = sha256(rb.read()).hexdigest() + +assert h == '70cbf37f84610967f2ca72dadb95456fdd8b6c72cdd6dc7372c50f525889ff0c', h diff --git a/fp16/kokoro-v0_19-half.pth b/fp16/kokoro-v0_19-half.pth new file mode 100644 index 0000000000000000000000000000000000000000..4501f2d872bc857e5b3b6aefc5ea86e6a32edf8a --- /dev/null +++ b/fp16/kokoro-v0_19-half.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7b1b7a588b6d7cde0c93cbc04bcfc8eb67ab179b755dbd3e8e74be11a31914b +size 3407872 diff --git a/frontend/.DS_Store b/frontend/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4021074617dbe321adc316cc8a43abc8e721b6a1 Binary files /dev/null and b/frontend/.DS_Store differ diff --git a/frontend/.env b/frontend/.env new file mode 100644 index 0000000000000000000000000000000000000000..7f21876bc844deb23b72cd65d3c4f729cdef72a9 --- /dev/null +++ b/frontend/.env @@ -0,0 +1 @@ +REACT_APP_WEBSOCKET_URL = localhost:8080 \ No newline at end of file diff --git a/frontend/.gitignore b/frontend/.gitignore new file 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0000000000000000000000000000000000000000..a4e47a6545bc15971f8f63fba70e4013df88a664 Binary files /dev/null and b/frontend/public/favicon.ico differ diff --git a/frontend/public/favicon.png b/frontend/public/favicon.png new file mode 100644 index 0000000000000000000000000000000000000000..a4e47a6545bc15971f8f63fba70e4013df88a664 Binary files /dev/null and b/frontend/public/favicon.png differ diff --git a/frontend/public/index.html b/frontend/public/index.html new file mode 100644 index 0000000000000000000000000000000000000000..d9073fcfe555c22091458b6be1859cb3adfc324c --- /dev/null +++ b/frontend/public/index.html @@ -0,0 +1,44 @@ + + + + + + + + + + + + + + realtime-tts + + + +
+ + + diff --git a/frontend/public/logo192.png b/frontend/public/logo192.png new file mode 100644 index 0000000000000000000000000000000000000000..fc44b0a3796c0e0a64c3d858ca038bd4570465d9 Binary files /dev/null and b/frontend/public/logo192.png differ diff --git a/frontend/public/logo512.png b/frontend/public/logo512.png new file mode 100644 index 0000000000000000000000000000000000000000..a4e47a6545bc15971f8f63fba70e4013df88a664 Binary files /dev/null and b/frontend/public/logo512.png differ diff --git a/frontend/public/manifest.json b/frontend/public/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0ab689fe15ec85f881c09e57387d17e06f115729 --- /dev/null +++ b/frontend/public/manifest.json @@ -0,0 +1,25 @@ +{ + "short_name": "realtime-tts", + "name": "Here is the first version of realtime-tts", + "icons": [ + { + "src": "favicon.ico", + "sizes": "64x64 32x32 24x24 16x16", + "type": "image/x-icon" + }, + { + "src": "favicon.png", + "type": "image/png", + "sizes": "192x192" + }, + { + "src": "favicon.png", + "type": "image/png", + "sizes": "512x512" + } + ], + "start_url": ".", + "display": "standalone", + "theme_color": "#000000", + "background_color": "#ffffff" +} diff --git a/frontend/public/robots.txt b/frontend/public/robots.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9e57dc4d41b9b46e05112e9f45b7ea6ac0ba15e --- /dev/null +++ b/frontend/public/robots.txt @@ -0,0 +1,3 @@ +# https://www.robotstxt.org/robotstxt.html +User-agent: * +Disallow: diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000000000000000000000000000000000000..a4ae5874c598af090f8384d7247aa28242865739 --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,66 @@ +import { lazy, Suspense} from "react"; +import { BrowserRouter as Router, Routes, Route } from "react-router-dom"; +import { Toaster} from "react-hot-toast"; +import css from "./App.module.css"; +import Lottie from "lottie-react"; +import LoadingAnimation from "./assets/animations/Loading1.json"; +const Playground = lazy(() => import("./pages/Playground/Playground.jsx")); +const NotFound = lazy(() => import("./pages/404/NotFound.jsx")); + +function App() { + return ( + <> + + + + + +
+ +
+ + } + > + + + + } + /> + + +
+ +
+ + } + > + + + + } + /> + + + + ); +} + +export default App; diff --git a/frontend/src/App.module.css b/frontend/src/App.module.css new file mode 100644 index 0000000000000000000000000000000000000000..b1fa36421f01935d8629e2914de0e4889b6a2ef2 --- /dev/null +++ b/frontend/src/App.module.css @@ -0,0 +1,11 @@ +.animation{ + width: 100vw; + max-height: 100vh; + display: flex; + justify-content: center; + align-content: center; + overflow: hidden; +} +.lottie{ + color: white; +} diff --git a/frontend/src/App.test.js b/frontend/src/App.test.js new file mode 100644 index 0000000000000000000000000000000000000000..1f03afeece5ac28064fa3c73a29215037465f789 --- /dev/null +++ b/frontend/src/App.test.js @@ -0,0 +1,8 @@ +import { render, screen } from '@testing-library/react'; +import App from './App'; + +test('renders learn react link', () => { + render(); + const linkElement = screen.getByText(/learn react/i); + expect(linkElement).toBeInTheDocument(); +}); diff --git a/frontend/src/assets/.DS_Store b/frontend/src/assets/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/frontend/src/assets/.DS_Store differ diff --git a/frontend/src/assets/animations/Loading1.json b/frontend/src/assets/animations/Loading1.json new file mode 100644 index 0000000000000000000000000000000000000000..625fcb1a95f3c4b6517ad184d7378644275c21d3 --- /dev/null +++ b/frontend/src/assets/animations/Loading1.json @@ -0,0 +1 @@ +{"v":"5.8.1","fr":24,"ip":0,"op":31,"w":1080,"h":1080,"nm":"Blocks loading","ddd":0,"assets":[],"layers":[{"ddd":0,"ind":1,"ty":4,"nm":"Layer 6","parent":4,"sr":1,"ks":{"o":{"a":0,"k":100,"ix":11},"r":{"a":1,"k":[{"i":{"x":[0.833],"y":[0.833]},"o":{"x":[0.167],"y":[0.167]},"t":10,"s":[0]},{"t":20,"s":[-180]}],"ix":10},"p":{"a":1,"k":[{"i":{"x":0.833,"y":0.833},"o":{"x":0.167,"y":0.167},"t":10,"s":[195.947,191.8,0],"to":[-32.683,0,0],"ti":[32.683,0,0]},{"t":20,"s":[-0.153,191.8,0]}],"ix":2,"l":2},"a":{"a":0,"k":[0,0,0],"ix":1,"l":2},"s":{"a":0,"k":[100,100,100],"ix":6,"l":2}},"ao":0,"shapes":[{"ty":"gr","it":[{"ind":0,"ty":"sh","ix":1,"ks":{"a":0,"k":{"i":[[0,0],[0,0],[0,0],[0,0]],"o":[[0,0],[0,0],[0,0],[0,0]],"v":[[74.16,74.16],[-74.16,74.16],[-74.16,-74.16],[74.16,-74.16]],"c":true},"ix":2},"nm":"Path 1","mn":"ADBE Vector Shape - 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Group","hd":false},{"ty":"st","c":{"a":0,"k":[0.137254908681,0.121568627656,0.1254902035,1],"ix":3},"o":{"a":0,"k":100,"ix":4},"w":{"a":0,"k":18,"ix":5},"lc":1,"lj":1,"ml":10,"bm":0,"nm":"Stroke 1","mn":"ADBE Vector Graphic - Stroke","hd":false},{"ty":"tr","p":{"a":0,"k":[0,0],"ix":2},"a":{"a":0,"k":[0,0],"ix":1},"s":{"a":0,"k":[100,100],"ix":3},"r":{"a":0,"k":0,"ix":6},"o":{"a":0,"k":100,"ix":7},"sk":{"a":0,"k":0,"ix":4},"sa":{"a":0,"k":0,"ix":5},"nm":"Transform"}],"nm":"Group 1","np":2,"cix":2,"bm":0,"ix":1,"mn":"ADBE Vector Group","hd":false}],"ip":0,"op":31,"st":0,"bm":0}],"markers":[]} \ No newline at end of file diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..5875ab725c6891a2603ca473ec9c1823451e2363 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,18 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + + +body { + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', + sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +code { + font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', + monospace; +} diff --git a/frontend/src/index.js b/frontend/src/index.js new file mode 100644 index 0000000000000000000000000000000000000000..d563c0fb10ba0e42724b21286eb546ee4e5734fc --- /dev/null +++ b/frontend/src/index.js @@ -0,0 +1,17 @@ +import React from 'react'; +import ReactDOM from 'react-dom/client'; +import './index.css'; +import App from './App'; +import reportWebVitals from './reportWebVitals'; + +const root = ReactDOM.createRoot(document.getElementById('root')); +root.render( + + + +); + +// If you want to start measuring performance in your app, pass a function +// to log results (for example: reportWebVitals(console.log)) +// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals +reportWebVitals(); diff --git a/frontend/src/logo.svg b/frontend/src/logo.svg new file mode 100644 index 0000000000000000000000000000000000000000..9dfc1c058cebbef8b891c5062be6f31033d7d186 --- /dev/null +++ b/frontend/src/logo.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/pages/404/NotFound.jsx b/frontend/src/pages/404/NotFound.jsx new file mode 100644 index 0000000000000000000000000000000000000000..b653030e1eafac14026b5b2a3c1eda54a3048eab --- /dev/null +++ b/frontend/src/pages/404/NotFound.jsx @@ -0,0 +1,32 @@ +import { useNavigate } from "react-router-dom"; + +export default function NotFound() { + const navigate = useNavigate(); + + const handlegohome = () => { + navigate("/"); + }; + return ( + <> +
+
+

404

+

+ Page not found +

+

+ Sorry, we couldn’t find the page you’re looking for. +

+
+

+ Go back home +

+
+
+
+ + ); +} diff --git a/frontend/src/pages/Playground/Playground.jsx b/frontend/src/pages/Playground/Playground.jsx new file mode 100644 index 0000000000000000000000000000000000000000..732f14e650c34cb02aef868fee7f85185dafafb7 --- /dev/null +++ b/frontend/src/pages/Playground/Playground.jsx @@ -0,0 +1,552 @@ +import React, { useEffect, useRef, useState } from "react"; +import css from "./Playground.module.scss"; +import toast from "react-hot-toast"; +import { v4 as uuidv4 } from "uuid"; +import { useNavigate } from "react-router-dom"; +import { GiBroom } from "react-icons/gi"; +import { RiAiGenerate } from "react-icons/ri"; + +const voices = [ + { + name: "JILL", + value: "jill", + }, + { + name: "JACK", + value: "jack", + }, +]; + +const Playground = () => { + const navigate = useNavigate(); + const [temperature, setTemperature] = useState(0.7); + const [threshold, setThreshold] = useState(0.5); + const [prefixPadding, setPrefixPadding] = useState(300); + const [silenceDuration, setSilenceDuration] = useState(100); + const [mobileView, setmobileView] = useState(false); + const [generating, setgenerating] = useState(false); + const [isVoices, setisVoices] = useState(false); + const [selectedVoice, setselectedVoice] = useState(voices[0]); + const [ismicopen, setismicopen] = useState(false); + const [ismic, setismic] = useState(false); + const [audioStream, setAudioStream] = useState(null); + const [audioContext, setAudioContext] = useState(null); + const sourceRef = useRef(null); + const audioContextRef = useRef(null); + const playing = useRef(false); + const [isplaying, setisplaying] = useState(false); + const lastshifted = useRef(null); + const bufferQueue = useRef([]); + const socket = useRef(null); + const isgrpc = useRef(null); + const [isgrpcs, setisgrpc] = useState(false); + const [socketConnected, setsocketConnected] = useState(false); + const [audioContextState, setaudioContextState] = useState(false); + if (!sessionStorage.getItem("sessionId")) { + const sessionId = uuidv4(); + sessionStorage.setItem("sessionId", sessionId); + } + const [session, setsession] = useState(sessionStorage.getItem("sessionId")); + const [system_prompt, setsetsystemPrompt] = useState(""); + const [elapsedTime, setElapsedTime] = useState(0); + const [latency, setlatency] = useState(0); + + const [chathistory, setchathistory] = useState([]); + const msgref = useRef(null); + + useEffect(() => { + if (chathistory) { + msgref?.current?.scrollIntoView({ behavior: "smooth" }); + } + }, [chathistory]); + + const handlePlay = async (da) => { + try { + if (!isgrpc.current) { + return; + } + playing.current = true; + setisplaying(true); + + const base64Data = bufferQueue.current.shift(); + lastshifted.current = base64Data; + + const bytes = new Uint8Array( + atob(base64Data) + .split("") + .map((char) => char.charCodeAt(0)) + ); + + let arrayBuffer = bytes.buffer; + + const metadataEndIndex = bytes.indexOf(0); + let metadata = new TextDecoder().decode(bytes.slice(0, metadataEndIndex)); + metadata = JSON.parse(metadata); + + const { session_id, sequence_id, transcript } = metadata; + if (sequence_id !== "-2") { + if (socket.interval) { + clearInterval(socket.interval); + socket.interval = null; + setlatency((prev) => prev + 150); + } + arrayBuffer = arrayBuffer.slice( + metadataEndIndex + 1, + arrayBuffer.length + ); + + try { + if (audioContextRef.current.state === "suspended") { + await audioContextRef.current.resume(); + } + const audioBuffer = await audioContextRef.current.decodeAudioData( + arrayBuffer + ); + + if (sourceRef.current) { + sourceRef.current.disconnect(); + } + + const source = audioContextRef.current.createBufferSource(); + source.buffer = audioBuffer; + source.connect(audioContextRef.current.destination); + source.start(0); + sourceRef.current = source; + sourceRef.current.session_id = session_id; + sourceRef.current.sequence_id = sequence_id; + sourceRef.current.transcript = transcript; + + sourceRef.current.onended = () => { + lastshifted.current = null; + if ( + socket.current.readyState === WebSocket.OPEN && + sourceRef?.current?.sequence_id + ) { + socket.current.send( + JSON.stringify({ + type: "status", + msg: { + session_id: sourceRef?.current?.session_id, + sequence_id: sourceRef?.current?.sequence_id, + transcript: sourceRef?.current?.transcript, + }, + }) + ); + } + + if (bufferQueue.current.length > 0) { + playing.current = true; + setisplaying(true); + const da = Date.now(); + handlePlay(da); + } else { + playing.current = false; + setisplaying(false); + } + }; + } catch (error) { + console.error("Error decoding audio data:", error); + } + } else { + const startTime = Date.now(); + const intervall = setInterval(() => { + setlatency(Date.now() - startTime); + }, 10); + socket.interval = intervall; + if (bufferQueue.current.length > 0) { + playing.current = true; + setisplaying(true); + const da = Date.now(); + handlePlay(da); + } else { + playing.current = false; + setisplaying(false); + } + } + } catch (error) { + console.error("Error in handlePlay: ", error); + } + }; + + const handlemicchange = async () => { + if (ismicopen && sourceRef.current) { + sourceRef.current.onended = null; + sourceRef.current.stop(); + sourceRef.current.disconnect(); + sourceRef.current = null; + bufferQueue.current = []; + } + if (!ismicopen) { + setlatency(0); + setismicopen(true); + await connectToRealtimeTTS(); + socket.current.send( + JSON.stringify({ + type: "start", + msg: JSON.stringify({ + temperature: temperature, + prefixPadding: prefixPadding, + silenceDuration: silenceDuration, + voice: selectedVoice?.value, + threshold: threshold, + system_prompt: system_prompt, + sessionId: session, + }), + }) + ); + } else { + stopAudioStream(); + } + ismic ? setismic(false) : setismic(true); + }; + + // useEffect(() => { + const connectToRealtimeTTS = async () => { + return new Promise((resolve, reject) => { + try { + const audioContext = new (window.AudioContext || + window.webkitAudioContext)(); + audioContextRef.current = audioContext; + setaudioContextState(true); + const websocketURL = process.env.REACT_APP_WEBSOCKET_URL.includes( + "localhost" + ) + ? `ws://${process.env.REACT_APP_WEBSOCKET_URL}/v2v` + : `wss://${process.env.REACT_APP_WEBSOCKET_URL}/v2v`; + const ws = new WebSocket(websocketURL); + ws.onopen = () => { + // console.log("WebSocket connected"); + // socket.current = ws; + // setsocketConnected(true); + // toast.success("Now You can speak..."); + }; + + ws.onmessage = async (event) => { + try { + const data = JSON.parse(event.data); + + const { type, msg } = data; + + switch (type) { + case "initial": + socket.current = ws; + setsocketConnected(true); + resolve(null); + break; + case "media": + const da = Date.now(); + bufferQueue.current.push(msg); + + if (!playing.current && bufferQueue.current.length > 0) { + handlePlay(da); + } + break; + case "info": + toast.error(msg); + break; + case "ready": + isgrpc.current = true; + setisgrpc(true); + await startAudioStream(); + break; + case "pause": + if (sourceRef.current) { + sourceRef.current.onended = null; + sourceRef.current.stop(); + sourceRef.current.disconnect(); + sourceRef.current = null; + } + playing.current = false; + setgenerating(true); + setisplaying(false); + break; + case "continue": + if (lastshifted.current) { + bufferQueue.current.unshift(lastshifted.current); + lastshifted.current = null; + } + setgenerating(false); + const daa = Date.now(); + handlePlay(daa); + if (socket.interval) { + clearInterval(socket.interval); + socket.interval = null; + } + break; + case "clear": + bufferQueue.current = []; + playing.current = false; + setgenerating(false); + setisplaying(false); + + if (sourceRef.current) { + sourceRef.current.onended = null; + sourceRef.current.stop(); + sourceRef.current.disconnect(); + sourceRef.current = null; + } + break; + case "end": + try { + audioContext + .close() + .then(() => { + setaudioContextState(false); + if (ismicopen) { + toast.error("Please restart the conversation."); + } + }) + .catch((err) => { + if (ismicopen) { + toast.error("Please restart the conversation."); + } + }); + await stopAudioStream(); + setismicopen(false); + } catch (error) { + // console.error("Error in closing audioContext."); + } + break; + case "chathistory": + setchathistory(msg); + break; + default: + break; + } + } catch (error) { + // console.error("Error in websocket media."); + } + }; + + ws.onclose = async () => { + try { + await audioStream.getTracks().forEach((track) => track.stop()); + setAudioStream(null); + setElapsedTime(0); + } catch (err) { + // console.error(err); + } + }; + + ws.onerror = (err) => { + // console.error("Websocket Error", err); + }; + } catch (err) { + console.error("Error in making websocket connection."); + reject(null); + } + }); + }; + + function floatTo16BitPCM(input) { + const output = new Int16Array(input.length); + for (let i = 0; i < input.length; i++) { + const sample = Math.max(-1, Math.min(1, input[i])); + output[i] = sample < 0 ? sample * 0x8000 : sample * 0x7fff; + } + return output.buffer; + } + + const startAudioStream = async () => { + try { + const startTime = Date.now(); + setchathistory([]); + const interval = setInterval(() => { + setElapsedTime(Date.now() - startTime); + }, 1000); + if (!socket.current) { + toast.error("Please try again.. Socket"); + return; + } + audioContextRef.current = new (window.AudioContext || + window.webkitAudioContext)(); + setismicopen(true); + const stream = await navigator.mediaDevices.getUserMedia({ audio: true }); + setAudioStream(stream); + + const audioContext = new AudioContext({ + sampleRate: 16000, + }); + + setAudioContext(audioContext); + const audioInput = audioContext.createMediaStreamSource(stream); + const bufferSize = 2048; + const scriptProcessorNode = audioContext.createScriptProcessor( + bufferSize, + 1, + 1 + ); + + scriptProcessorNode.onaudioprocess = async (e) => { + const inputData = e.inputBuffer.getChannelData(0); + const l16Data = floatTo16BitPCM(inputData); + try { + if (!isgrpc.current) { + try { + if (audioContextState) { + audioContext + .close() + .then(() => { + setaudioContextState(false); + toast.error("Please restart the conversation."); + }) + .catch((err) => { + toast.error("Please restart the conversation."); + // console.error(err); + }); + await stopAudioStream(); + toast.error("Please start again."); + setismicopen(false); + } + } catch (error) { + // console.error("Error in closing audioContext."); + } + } + if ( + isgrpc.current && + socket.current && + socket.current.readyState === WebSocket.OPEN + ) { + // if (socket.current.grpc) { + socket.current.send(l16Data); + } + } catch (err) { + // console.error("Error in sending buffer."); + } + }; + + audioInput.connect(scriptProcessorNode); + scriptProcessorNode.connect(audioContext.destination); + } catch (error) { + // console.error("Error accessing microphone:", error); + } + }; + + const stopAudioStream = async () => { + setgenerating(false); + setismicopen(false); + setElapsedTime(0); + setisgrpc(false); + bufferQueue.current = []; + if (socket.current.readyState === WebSocket.OPEN) { + socket.current.close(); + } + if (socket.interval) { + clearInterval(socket.interval); + socket.current = null; + } + if ( + socket.current && + audioStream && + socket.current.readyState === WebSocket.OPEN + ) { + try { + if (audioStream) { + await audioStream.getTracks().forEach((track) => track.stop()); + setAudioStream(null); + } + } catch (err) { + // console.error(err); + } + try { + if (socket.current.readyState === WebSocket.OPEN) { + isgrpc.current = false; + socket.current.send(JSON.stringify({ type: "stop", msg: "stop" })); + } + audioContext + .close() + .then(() => { + // console.log("AudioContext closed."); + }) + .catch((err) => { + // console.error("Error in closing the audioContext.", err); + }); + } catch (err) { + // console.error("Error in closing the audioContext."); + } + } + }; + const getButtonText = () => { + if (!ismicopen) { + return "Start"; + } + if (ismicopen && !isgrpcs) { + return "Starting"; + } + if (isgrpcs) { + return "Stop"; + } + return ""; + }; + + const formatTime = (milliseconds) => { + let totalSeconds = Math.floor(milliseconds / 1000); + let hours = Math.floor(totalSeconds / 3600); + let minutes = Math.floor((totalSeconds % 3600) / 60); + let seconds = totalSeconds % 60; + + return `${String(hours).padStart(2, "0")}:${String(minutes).padStart( + 2, + "0" + )}:${String(seconds).padStart(2, "0")}`; + }; + + return ( +
+
+
+
+ +
+
+ {chathistory.length > 0 && + chathistory.map((item, index) => { + return ( + <> +
+ {/* {item.timestamp} */} +
+
{item.speaker}
+
{item.content}
+ {/* {item.content} */} +
+
+
+ + ); + })} +
+
+ {socketConnected || !socketConnected ? ( +
+
+
+ {/* {!ismicopen ? "00:00:00" : formatTime(elapsedTime)} */} + {`${latency} ms`} +
+ +
+
+ ) : ( +
Connecting...
+ )} +
+
+
+
+ ); +}; + +export default Playground; diff --git a/frontend/src/pages/Playground/Playground.module.scss b/frontend/src/pages/Playground/Playground.module.scss new file mode 100644 index 0000000000000000000000000000000000000000..9c36c61a78e449a089f4d4edf5a7bdb9a944aca6 --- /dev/null +++ b/frontend/src/pages/Playground/Playground.module.scss @@ -0,0 +1,687 @@ +.main { + padding: 10px; + background-color: rgb(0, 0, 0); + flex-wrap: wrap; + word-break: keep-all; + color: white; + min-height: 100vh; + overflow: hidden; +} + +.container { + background-color: rgb(32, 32, 32); + display: flex; + flex-direction: row; + justify-content: space-between; + max-height: 98vh; + min-height: 98vh; + padding: 10px 10px; + font-family: Arial, sans-serif; + width: 100%; + border: 1px solid rgb(110, 110, 110); + border-radius: 10px; +} + +.header { + padding: 0px 16px; + width: 70%; +} + +.voices { + background-color: transparent; + border: 1px solid white; + width: 100%; + margin: 0px 0px 3px 0px; + border-radius: 5px; + padding: 0px 10px; + outline: none; + position: relative; + ::selection { + display: none; + } + + div { + display: flex; + justify-content: space-between; + align-items: center; + + img { + width: 40px; + height: 30px; + } + } + + .voicesOptions { + position: absolute; + width: 100%; + height: fit-content; + left: 0; + top: 110%; + z-index: 1; + padding: 0; + list-style: none; + background-color: transparent; + backdrop-filter: blur(20px); + border: 1px solid white; + border-radius: 10px; + display: flex; + flex-direction: column; + + ul { + list-style: none; + border-radius: 10px; + padding: 5px; + width: 100%; + li { + border-radius: 10px; + margin: 5px auto; + padding: 5px 15px; + font-size: 16px; + width: 100%; + border: 1px solid transparent; + display: flex; + justify-content: space-between; + align-items: center; + cursor: pointer; + + div { + width: 90%; + } + img { + cursor: pointer; + } + } + + .selected { + border: 1px solid white; + } + + li:hover { + border: 1px solid white; + } + } + } +} + +.heading { + color: rgb(222, 222, 222); + font-weight: 700; + font-family: "Courier New", Courier, monospace; + font-size: 1.2rem; +} + +.setting { + display: none; +} + +.nav { + display: flex; + justify-content: space-between; + background-color: black; + align-items: center; + padding: 0; + display: none; + img { + width: 20px; + height: 20px; + cursor: pointer; + + &:hover { + transform: scale(1.2); + + .toolkit { + display: flex; + position: absolute; + top: 40px; + right: 10px; + font-size: 13px; + background-color: black; + color: white; + padding: 2px; + transition: opacity 0.3s ease; + } + } + } + + .toolkit { + display: none; + z-index: 10; + } +} + +.realtime { + display: flex; + flex-direction: column; + justify-content: center; + height: 90vh; + width: 100%; + + .clarbtn { + display: flex; + justify-content: flex-end; + button { + padding: 7px 10px; + background-color: #575757d7; + color: white; + border: none; + font-size: 1rem; + display: flex; + width: fit-content; + justify-content: center; + align-items: center; + border-radius: 5px; + cursor: pointer; + &:hover { + background-color: #464646; + } + } + + .broom { + max-width: 20px; + max-height: 20px; + margin-right: 10px; + } + } + + .conversation { + padding: 8px; + margin-top: 8px; + display: flex; + justify-content: flex-start; + align-items: flex-start; + gap: 20px; + flex-direction: column; + max-height: 70vh; + min-height: 70vh; + width: 90%; + overflow: scroll; + + scrollbar-width: none; /* For Firefox */ + -ms-overflow-style: none; /* For Internet Explorer and Edge */ + + &::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ + } + + .markdown { + display: flex; + flex-direction: column; + } + + .message { + display: flex; + height: fit-content; + gap: 20px; + + .msginfo { + color: black; + height: fit-content; + } + } + + .msg { + display: flex; + flex-direction: column; + height: fit-content; + align-items: center; + gap: 10px; + + div { + display: flex; + justify-content: flex-start; + width: 100%; + } + } + .speaker { + font-weight: 700; + } + } +} + +.speakNow { + display: flex;; + align-items: center; + justify-content: center; + flex-direction: column; + margin: 10px 0px 10px 0px; + background-blend-mode: darken; + gap: 0px; + + scrollbar-width: none; /* For Firefox */ + -ms-overflow-style: none; /* For Internet Explorer and Edge */ + + &::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ + } +} + +.speakNow::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ +} + +.mic_border { + display: flex; + justify-content: center; + align-items: center; + flex-direction: column; +} + +.controls { + width: 30%; + border: 1px solid rgb(165, 165, 165); + padding: 10px; + border-radius: 10px; + + textarea { + width: 100%; + min-height: 200px; + outline: none; + border-radius: 10px; + background-color: rgb(113, 113, 113); + padding: 10px; + font-family: "Courier New", Courier, monospace; + text-decoration: none; + font-weight: 700; + overflow: scroll; + } + + textarea::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ + } + + textarea::placeholder { + color: rgb(209, 209, 209); + } + + h4 { + margin-bottom: 10px; + font-size: 1.1rem; + position: relative; + display: flex; + justify-content: flex-start; + align-items: center; + .parainfoContent { + display: none; /* Hidden by default */ + } + .parametersInfo { + margin-left: 10px; + .infoicon { + color: white; + opacity: 0.5; + width: fit-content; + cursor: pointer; + transition: opacity 0.3s ease; + &:hover { + opacity: 1; + + .parainfoContent { + display: flex; + position: absolute; + top: 120%; /* Adjust according to where you want the content to appear */ + left: 0px; + background-color: #ffffff; /* Example background color */ + padding: 5px; + box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2); + z-index: 1; + font-size: 0.75rem; + color: #000000; + width: 200px; + height: fit-content; + text-align: center; + border-radius: 10px; + } + } + } + } + } + + p { + margin-bottom: 20px; + } +} + +.inputGroup { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 15px; +} + +.voiceToggle { + display: flex; + align-items: center; + + span { + margin-right: 10px; + color: #000000; + /* Matching the light blue color */ + } +} + +.sliderGroup { + margin-top: 10px; + margin-bottom: 20px; + width: 100%; + + .sliderinfo { + display: flex; + width: 100%; + justify-content: space-between; + align-items: center; + position: relative; + + .parainfoContent { + display: none; /* Hidden by default */ + } + + label { + width: fit-content; + margin-right: 10px; + font-weight: 500; + font-family: sans-serif; + display: flex; + justify-content: center; + align-items: center; + + .parametersInfo { + margin-left: 10px; + .infoicon { + color: white; + opacity: 0.5; + width: fit-content; + cursor: pointer; + transition: opacity 0.3s ease; + &:hover { + opacity: 1; + + .parainfoContent { + display: flex; + position: absolute; + top: 120%; /* Adjust according to where you want the content to appear */ + left: 0; + background-color: #fff; /* Example background color */ + padding: 5px; + border: 1px solid #ddd; + box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2); + z-index: 1; + font-size: 0.75rem; + color: #333; + max-width: 300px; + height: fit-content; + text-align: center; + } + } + } + } + } + + span { + color: rgb(153, 153, 153); + font-family: sans-serif; + font-size: 0.8rem; + } + } + + input[type="range"] { + flex: 1; + margin-right: 0 10px; + width: 100%; + -webkit-appearance: none; /* Remove default styles */ + background: transparent; /* Row color */ + } + + input[type="range"]::-webkit-slider-runnable-track { + width: 100%; + height: 4px; /* Adjust the thickness of the row */ + background: rgb(117, 117, 117); /* Row color */ + } + + input[type="range"]::-webkit-slider-thumb { + -webkit-appearance: none; /* Remove default styles */ + height: 15px; + width: 15px; + background: rgb(255, 255, 255); /* Thumb color */ + cursor: pointer; + border-radius: 50%; /* Make the thumb round */ + margin-top: -6px; + border: 1px solid rgb(121, 121, 121); + cursor: grabbing; + } + + input[type="range"]:focus { + outline: none; /* Remove the outline on focus */ + } + + span { + width: 60px; + text-align: right; + color: black; + } +} + +.startstop { + display: flex; + justify-content: space-evenly; + align-items: center; + gap: 10px; + background-color: rgb(62, 62, 62); + box-shadow: 0px 0px 1px rgb(108, 108, 108); + border-radius: 10px; + max-width: 80vw; + width: fit-content; + padding: 10px 10px; + overflow: scroll; + + scrollbar-width: none; /* For Firefox */ + -ms-overflow-style: none; /* For Internet Explorer and Edge */ + + &::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ + } + + .totaltime { + width: fit-content; + } + + .generateIcon { + max-width: 20px; + } + + .generating { + animation: generateanimation 1s infinite ease; + } + + .notgeneerating { + color: rgb(119, 119, 119); + } + + button { + padding: 10px 0px; + background-color: #01b1aed7; + color: white; + border: none; + border-radius: 5px; + min-width: 80px; + cursor: pointer; + &:hover { + background-color: #00908ed7; + } + } +} + +.selectMic { + padding: 8px; + border-radius: 5px; +} + +@media (max-width: 771px) { + .container { + width: 100%; + padding: 0; + overflow: hidden; + } + .header { + width: 100%; + padding: 0px; + } + + .marginclear { + margin-right: 20px; + } + + .nav { + display: flex; + width: 100%; + padding: 10px 10px; + font-size: 1rem; + height: 5vh; + margin-bottom: 1vh; + + img { + width: 30px; + height: 30px; + } + } + + .realtime { + max-height: 85vh; + overflow: hidden; + + .conversation { + overflow: hidden; + max-height: 63vh; + min-height: 63vh; + overflow: hidden; + scrollbar-width: none; /* For Firefox */ + -ms-overflow-style: none; /* For Internet Explorer and Edge */ + } + + .conversation::-webkit-scrollbar { + display: none; /* For Chrome, Safari, and Opera */ + } + } + + .controls { + display: flex; + position: absolute; + justify-content: center; + align-items: center; + flex-direction: column; + background-color: rgb(0, 0, 0); + height: 80vh; + display: none; + } + + .setting { + display: flex; + } + .controlsMobile { + display: flex; + position: absolute; + flex-direction: column; + background-color: black; + gap: 5px; + z-index: 10; + height: 100vh; + padding: 10px; + margin: 0; + top: 0; + right: 0; + overflow: scroll; + width: 100%; + animation: slidein 1s ease; + + .contolsMobile { + display: flex; + justify-content: space-between; + align-items: center; + width: 100%; + min-height: 50px; + + .heading { + position: relative; + display: flex; + justify-content: flex-start; + align-items: center; + .parainfoContent { + display: none; /* Hidden by default */ + } + .parametersInfo { + margin-left: 10px; + .infoicon { + color: white; + opacity: 0.5; + width: fit-content; + cursor: pointer; + transition: opacity 0.3s ease; + &:hover { + opacity: 1; + + .parainfoContent { + display: flex; + position: absolute; + top: 120%; /* Adjust according to where you want the content to appear */ + left: 0px; + background-color: #ffffff; /* Example background color */ + padding: 5px; + box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2); + z-index: 1; + font-size: 0.75rem; + color: #000000; + width: 200px; + height: fit-content; + text-align: center; + border-radius: 10px; + } + } + } + } + } + + img { + width: 30px; + height: 30px; + } + } + + textarea { + width: 100%; + min-height: 200px; + outline: none; + border-radius: 10px; + background-color: rgb(113, 113, 113); + padding: 10px; + font-family: "Courier New", Courier, monospace; + text-decoration: none; + font-weight: 700; + overflow: scroll; + } + + textarea::placeholder { + color: rgb(209, 209, 209); + } + + h4 { + margin-bottom: 10px; + font-size: 1.1rem; + } + + p { + margin-bottom: 20px; + } + } +} + +@keyframes slidein { + from { + transform: translateX(+100%); + } + to { + transform: translateX(0); + } +} + +@keyframes generateanimation { + from { + color: rgb(119, 119, 119); + } + to { + color: red; + } +} diff --git a/frontend/src/reportWebVitals.js b/frontend/src/reportWebVitals.js new file mode 100644 index 0000000000000000000000000000000000000000..5253d3ad9e6be6690549cb255f5952337b02401d --- /dev/null +++ b/frontend/src/reportWebVitals.js @@ -0,0 +1,13 @@ +const reportWebVitals = onPerfEntry => { + if (onPerfEntry && onPerfEntry instanceof Function) { + import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { + getCLS(onPerfEntry); + getFID(onPerfEntry); + getFCP(onPerfEntry); + getLCP(onPerfEntry); + getTTFB(onPerfEntry); + }); + } +}; + +export default reportWebVitals; diff --git a/frontend/src/setupTests.js b/frontend/src/setupTests.js new file mode 100644 index 0000000000000000000000000000000000000000..8f2609b7b3e0e3897ab3bcaad13caf6876e48699 --- /dev/null +++ b/frontend/src/setupTests.js @@ -0,0 +1,5 @@ +// jest-dom adds custom jest matchers for asserting on DOM nodes. +// allows you to do things like: +// expect(element).toHaveTextContent(/react/i) +// learn more: https://github.com/testing-library/jest-dom +import '@testing-library/jest-dom'; diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..5a666547730235fbc33297f57a27fddd3897e160 --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,10 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: [ + "./src/**/*.{js,jsx,ts,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} \ No newline at end of file diff --git a/istftnet.py b/istftnet.py new file mode 100644 index 0000000000000000000000000000000000000000..da29481368de41ce2a3ff9816c9bd3f11f3ab15e --- /dev/null +++ b/istftnet.py @@ -0,0 +1,523 @@ +# https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py +from scipy.signal import get_window +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +# https://github.com/yl4579/StyleTTS2/blob/main/Modules/utils.py +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) + +LRELU_SLOPE = 0.1 + +class AdaIN1d(nn.Module): + def __init__(self, style_dim, num_features): + super().__init__() + self.norm = nn.InstanceNorm1d(num_features, affine=False) + self.fc = nn.Linear(style_dim, num_features*2) + + def forward(self, x, s): + h = self.fc(s) + h = h.view(h.size(0), h.size(1), 1) + gamma, beta = torch.chunk(h, chunks=2, dim=1) + return (1 + gamma) * self.norm(x) + beta + +class AdaINResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64): + super(AdaINResBlock1, self).__init__() + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + self.adain1 = nn.ModuleList([ + AdaIN1d(style_dim, channels), + AdaIN1d(style_dim, channels), + AdaIN1d(style_dim, channels), + ]) + + self.adain2 = nn.ModuleList([ + AdaIN1d(style_dim, channels), + AdaIN1d(style_dim, channels), + AdaIN1d(style_dim, channels), + ]) + + self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]) + self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]) + + + def forward(self, x, s): + for c1, c2, n1, n2, a1, a2 in zip(self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2): + xt = n1(x, s) + xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2) # Snake1D + xt = c1(xt) + xt = n2(xt, s) + xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + +class TorchSTFT(torch.nn.Module): + def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'): + super().__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32)) + + def transform(self, input_data): + forward_transform = torch.stft( + input_data, + self.filter_length, self.hop_length, self.win_length, window=self.window.to(input_data.device), + return_complex=True) + + return torch.abs(forward_transform), torch.angle(forward_transform) + + def inverse(self, magnitude, phase): + inverse_transform = torch.istft( + magnitude * torch.exp(phase * 1j), + self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device)) + + return inverse_transform.unsqueeze(-2) # unsqueeze to stay consistent with conv_transpose1d implementation + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-wavefrom (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_thoreshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, upsample_scale, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0, + flag_for_pulse=False): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + self.flag_for_pulse = flag_for_pulse + self.upsample_scale = upsample_scale + + def _f02uv(self, f0): + # generate uv signal + uv = (f0 > self.voiced_threshold).type(torch.float32) + return uv + + def _f02sine(self, f0_values): + """ f0_values: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + # convert to F0 in rad. The interger part n can be ignored + # because 2 * np.pi * n doesn't affect phase + rad_values = (f0_values / self.sampling_rate) % 1 + + # initial phase noise (no noise for fundamental component) + rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ + device=f0_values.device) + rand_ini[:, 0] = 0 + rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini + + # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) + if not self.flag_for_pulse: +# # for normal case + +# # To prevent torch.cumsum numerical overflow, +# # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. +# # Buffer tmp_over_one_idx indicates the time step to add -1. +# # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi +# tmp_over_one = torch.cumsum(rad_values, 1) % 1 +# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 +# cumsum_shift = torch.zeros_like(rad_values) +# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + +# phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi + rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), + scale_factor=1/self.upsample_scale, + mode="linear").transpose(1, 2) + +# tmp_over_one = torch.cumsum(rad_values, 1) % 1 +# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 +# cumsum_shift = torch.zeros_like(rad_values) +# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 + + phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi + phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, + scale_factor=self.upsample_scale, mode="linear").transpose(1, 2) + sines = torch.sin(phase) + + else: + # If necessary, make sure that the first time step of every + # voiced segments is sin(pi) or cos(0) + # This is used for pulse-train generation + + # identify the last time step in unvoiced segments + uv = self._f02uv(f0_values) + uv_1 = torch.roll(uv, shifts=-1, dims=1) + uv_1[:, -1, :] = 1 + u_loc = (uv < 1) * (uv_1 > 0) + + # get the instantanouse phase + tmp_cumsum = torch.cumsum(rad_values, dim=1) + # different batch needs to be processed differently + for idx in range(f0_values.shape[0]): + temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] + temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] + # stores the accumulation of i.phase within + # each voiced segments + tmp_cumsum[idx, :, :] = 0 + tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum + + # rad_values - tmp_cumsum: remove the accumulation of i.phase + # within the previous voiced segment. + i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) + + # get the sines + sines = torch.cos(i_phase * 2 * np.pi) + return sines + + def forward(self, f0): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, + device=f0.device) + # fundamental component + fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) + + # generate sine waveforms + sine_waves = self._f02sine(fn) * self.sine_amp + + # generate uv signal + # uv = torch.ones(f0.shape) + # uv = uv * (f0 > self.voiced_threshold) + uv = self._f02uv(f0) + + # noise: for unvoiced should be similar to sine_amp + # std = self.sine_amp/3 -> max value ~ self.sine_amp + # . for voiced regions is self.noise_std + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + + # first: set the unvoiced part to 0 by uv + # then: additive noise + sine_waves = sine_waves * uv + noise + return sine_waves, uv, noise + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num, + sine_amp, add_noise_std, voiced_threshod) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x): + """ + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + """ + # source for harmonic branch + with torch.no_grad(): + sine_wavs, uv, _ = self.l_sin_gen(x) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + + # source for noise branch, in the same shape as uv + noise = torch.randn_like(uv) * self.sine_amp / 3 + return sine_merge, noise, uv +def padDiff(x): + return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) + + +class Generator(torch.nn.Module): + def __init__(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size): + super(Generator, self).__init__() + + self.num_kernels = len(resblock_kernel_sizes) + self.num_upsamples = len(upsample_rates) + resblock = AdaINResBlock1 + + self.m_source = SourceModuleHnNSF( + sampling_rate=24000, + upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size, + harmonic_num=8, voiced_threshod=10) + self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size) + self.noise_convs = nn.ModuleList() + self.noise_res = nn.ModuleList() + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + self.ups.append(weight_norm( + ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), + k, u, padding=(k-u)//2))) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel//(2**(i+1)) + for j, (k, d) in enumerate(zip(resblock_kernel_sizes,resblock_dilation_sizes)): + self.resblocks.append(resblock(ch, k, d, style_dim)) + + c_cur = upsample_initial_channel // (2 ** (i + 1)) + + if i + 1 < len(upsample_rates): # + stride_f0 = np.prod(upsample_rates[i + 1:]) + self.noise_convs.append(Conv1d( + gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2)) + self.noise_res.append(resblock(c_cur, 7, [1,3,5], style_dim)) + else: + self.noise_convs.append(Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1)) + self.noise_res.append(resblock(c_cur, 11, [1,3,5], style_dim)) + + + self.post_n_fft = gen_istft_n_fft + self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + self.reflection_pad = torch.nn.ReflectionPad1d((1, 0)) + self.stft = TorchSTFT(filter_length=gen_istft_n_fft, hop_length=gen_istft_hop_size, win_length=gen_istft_n_fft) + + + def forward(self, x, s, f0): + with torch.no_grad(): + f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t + + har_source, noi_source, uv = self.m_source(f0) + har_source = har_source.transpose(1, 2).squeeze(1) + har_spec, har_phase = self.stft.transform(har_source) + har = torch.cat([har_spec, har_phase], dim=1) + + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x_source = self.noise_convs[i](har) + x_source = self.noise_res[i](x_source, s) + + x = self.ups[i](x) + if i == self.num_upsamples - 1: + x = self.reflection_pad(x) + + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i*self.num_kernels+j](x, s) + else: + xs += self.resblocks[i*self.num_kernels+j](x, s) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) + phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) + return self.stft.inverse(spec, phase) + + def fw_phase(self, x, s): + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i*self.num_kernels+j](x, s) + else: + xs += self.resblocks[i*self.num_kernels+j](x, s) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.reflection_pad(x) + x = self.conv_post(x) + spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) + phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) + return spec, phase + + def remove_weight_norm(self): + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class AdainResBlk1d(nn.Module): + def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), + upsample='none', dropout_p=0.0): + super().__init__() + self.actv = actv + self.upsample_type = upsample + self.upsample = UpSample1d(upsample) + self.learned_sc = dim_in != dim_out + self._build_weights(dim_in, dim_out, style_dim) + self.dropout = nn.Dropout(dropout_p) + + if upsample == 'none': + self.pool = nn.Identity() + else: + self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1)) + + + def _build_weights(self, dim_in, dim_out, style_dim): + self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) + self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1)) + self.norm1 = AdaIN1d(style_dim, dim_in) + self.norm2 = AdaIN1d(style_dim, dim_out) + if self.learned_sc: + self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) + + def _shortcut(self, x): + x = self.upsample(x) + if self.learned_sc: + x = self.conv1x1(x) + return x + + def _residual(self, x, s): + x = self.norm1(x, s) + x = self.actv(x) + x = self.pool(x) + x = self.conv1(self.dropout(x)) + x = self.norm2(x, s) + x = self.actv(x) + x = self.conv2(self.dropout(x)) + return x + + def forward(self, x, s): + out = self._residual(x, s) + out = (out + self._shortcut(x)) / np.sqrt(2) + return out + +class UpSample1d(nn.Module): + def __init__(self, layer_type): + super().__init__() + self.layer_type = layer_type + + def forward(self, x): + if self.layer_type == 'none': + return x + else: + return F.interpolate(x, scale_factor=2, mode='nearest') + +class Decoder(nn.Module): + def __init__(self, dim_in=512, F0_channel=512, style_dim=64, dim_out=80, + resblock_kernel_sizes = [3,7,11], + upsample_rates = [10, 6], + upsample_initial_channel=512, + resblock_dilation_sizes=[[1,3,5], [1,3,5], [1,3,5]], + upsample_kernel_sizes=[20, 12], + gen_istft_n_fft=20, gen_istft_hop_size=5): + super().__init__() + + self.decode = nn.ModuleList() + + self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim) + + self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) + self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) + self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) + self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True)) + + self.F0_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)) + + self.N_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)) + + self.asr_res = nn.Sequential( + weight_norm(nn.Conv1d(512, 64, kernel_size=1)), + ) + + + self.generator = Generator(style_dim, resblock_kernel_sizes, upsample_rates, + upsample_initial_channel, resblock_dilation_sizes, + upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size) + + def forward(self, asr, F0_curve, N, s): + F0 = self.F0_conv(F0_curve.unsqueeze(1)) + N = self.N_conv(N.unsqueeze(1)) + + x = torch.cat([asr, F0, N], axis=1) + x = self.encode(x, s) + + asr_res = self.asr_res(asr) + + res = True + for block in self.decode: + if res: + x = torch.cat([x, asr_res, F0, N], axis=1) + x = block(x, s) + if block.upsample_type != "none": + res = False + + x = self.generator(x, s, F0_curve) + return x diff --git a/kokoro-v0_19.onnx b/kokoro-v0_19.onnx new file mode 100644 index 0000000000000000000000000000000000000000..1a00452ca6f0e02bc339cd73b410e91a9bbc2957 --- /dev/null +++ b/kokoro-v0_19.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ebef42457f7efee9b60b4f1d5aec7692f2925923948a0d7a2a49d2c9edf57e49 +size 345554732 diff --git a/kokoro-v0_19.pth b/kokoro-v0_19.pth new file mode 100644 index 0000000000000000000000000000000000000000..e2e0962644d70d3010d1d615e2146e0df8c86d76 --- /dev/null +++ b/kokoro-v0_19.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b0c392f87508da38fad3a2f9d94c359f1b657ebd2ef79f9d56d69503e470b0a +size 327211206 diff --git a/kokoro.py b/kokoro.py new file mode 100644 index 0000000000000000000000000000000000000000..8e405300ceabea1561475df71941ef9209435e46 --- /dev/null +++ b/kokoro.py @@ -0,0 +1,165 @@ +import phonemizer +import re +import torch +import numpy as np + +def split_num(num): + num = num.group() + if '.' in num: + return num + elif ':' in num: + h, m = [int(n) for n in num.split(':')] + if m == 0: + return f"{h} o'clock" + elif m < 10: + return f'{h} oh {m}' + return f'{h} {m}' + year = int(num[:4]) + if year < 1100 or year % 1000 < 10: + return num + left, right = num[:2], int(num[2:4]) + s = 's' if num.endswith('s') else '' + if 100 <= year % 1000 <= 999: + if right == 0: + return f'{left} hundred{s}' + elif right < 10: + return f'{left} oh {right}{s}' + return f'{left} {right}{s}' + +def flip_money(m): + m = m.group() + bill = 'dollar' if m[0] == '$' else 'pound' + if m[-1].isalpha(): + return f'{m[1:]} {bill}s' + elif '.' not in m: + s = '' if m[1:] == '1' else 's' + return f'{m[1:]} {bill}{s}' + b, c = m[1:].split('.') + s = '' if b == '1' else 's' + c = int(c.ljust(2, '0')) + coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence') + return f'{b} {bill}{s} and {c} {coins}' + +def point_num(num): + a, b = num.group().split('.') + return ' point '.join([a, ' '.join(b)]) + +def normalize_text(text): + text = text.replace(chr(8216), "'").replace(chr(8217), "'") + text = text.replace('«', chr(8220)).replace('»', chr(8221)) + text = text.replace(chr(8220), '"').replace(chr(8221), '"') + text = text.replace('(', '«').replace(')', '»') + for a, b in zip('、。！，：；？', ',.!,:;?'): + text = text.replace(a, b+' ') + text = re.sub(r'[^\S \n]', ' ', text) + text = re.sub(r' +', ' ', text) + text = re.sub(r'(?<=\n) +(?=\n)', '', text) + text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text) + text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text) + text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text) + text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text) + text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text) + text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text) + text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(? 510: + tokens = tokens[:510] + print('Truncated to 510 tokens') + ref_s = voicepack[len(tokens)] + out = forward(model, tokens, ref_s, speed) + ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens) + return out, ps + +def generate_full(model, text, voicepack, lang='a', speed=1, ps=None): + ps = ps or phonemize(text, lang) + tokens = tokenize(ps) + if not tokens: + return None + outs = [] + loop_count = len(tokens)//510 + (1 if len(tokens) % 510 != 0 else 0) + for i in range(loop_count): + ref_s = voicepack[len(tokens[i*510:(i+1)*510])] + out = forward(model, tokens[i*510:(i+1)*510], ref_s, speed) + outs.append(out) + outs = np.concatenate(outs) + ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens) + return outs, ps \ No newline at end of file diff --git a/models.py b/models.py new file mode 100644 index 0000000000000000000000000000000000000000..516ad7d4fa4367e23c0f64c14761b07b768d01ef --- /dev/null +++ b/models.py @@ -0,0 +1,372 @@ +# https://github.com/yl4579/StyleTTS2/blob/main/models.py +from istftnet import AdaIN1d, Decoder +from munch import Munch +from pathlib import Path +from plbert import load_plbert +from torch.nn.utils import weight_norm, spectral_norm +import json +import numpy as np +import os +import os.path as osp +import torch +import torch.nn as nn +import torch.nn.functional as F + +class LinearNorm(torch.nn.Module): + def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): + super(LinearNorm, self).__init__() + self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) + + torch.nn.init.xavier_uniform_( + self.linear_layer.weight, + gain=torch.nn.init.calculate_gain(w_init_gain)) + + def forward(self, x): + return self.linear_layer(x) + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = nn.Parameter(torch.ones(channels)) + self.beta = nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + x = x.transpose(1, -1) + x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) + return x.transpose(1, -1) + +class TextEncoder(nn.Module): + def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)): + super().__init__() + self.embedding = nn.Embedding(n_symbols, channels) + + padding = (kernel_size - 1) // 2 + self.cnn = nn.ModuleList() + for _ in range(depth): + self.cnn.append(nn.Sequential( + weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)), + LayerNorm(channels), + actv, + nn.Dropout(0.2), + )) + # self.cnn = nn.Sequential(*self.cnn) + + self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True) + + def forward(self, x, input_lengths, m): + x = self.embedding(x) # [B, T, emb] + x = x.transpose(1, 2) # [B, emb, T] + m = m.to(input_lengths.device).unsqueeze(1) + x.masked_fill_(m, 0.0) + + for c in self.cnn: + x = c(x) + x.masked_fill_(m, 0.0) + + x = x.transpose(1, 2) # [B, T, chn] + + input_lengths = input_lengths.cpu().numpy() + x = nn.utils.rnn.pack_padded_sequence( + x, input_lengths, batch_first=True, enforce_sorted=False) + + self.lstm.flatten_parameters() + x, _ = self.lstm(x) + x, _ = nn.utils.rnn.pad_packed_sequence( + x, batch_first=True) + + x = x.transpose(-1, -2) + x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) + + x_pad[:, :, :x.shape[-1]] = x + x = x_pad.to(x.device) + + x.masked_fill_(m, 0.0) + + return x + + def inference(self, x): + x = self.embedding(x) + x = x.transpose(1, 2) + x = self.cnn(x) + x = x.transpose(1, 2) + self.lstm.flatten_parameters() + x, _ = self.lstm(x) + return x + + def length_to_mask(self, lengths): + mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) + mask = torch.gt(mask+1, lengths.unsqueeze(1)) + return mask + + +class UpSample1d(nn.Module): + def __init__(self, layer_type): + super().__init__() + self.layer_type = layer_type + + def forward(self, x): + if self.layer_type == 'none': + return x + else: + return F.interpolate(x, scale_factor=2, mode='nearest') + +class AdainResBlk1d(nn.Module): + def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), + upsample='none', dropout_p=0.0): + super().__init__() + self.actv = actv + self.upsample_type = upsample + self.upsample = UpSample1d(upsample) + self.learned_sc = dim_in != dim_out + self._build_weights(dim_in, dim_out, style_dim) + self.dropout = nn.Dropout(dropout_p) + + if upsample == 'none': + self.pool = nn.Identity() + else: + self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1)) + + + def _build_weights(self, dim_in, dim_out, style_dim): + self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) + self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1)) + self.norm1 = AdaIN1d(style_dim, dim_in) + self.norm2 = AdaIN1d(style_dim, dim_out) + if self.learned_sc: + self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) + + def _shortcut(self, x): + x = self.upsample(x) + if self.learned_sc: + x = self.conv1x1(x) + return x + + def _residual(self, x, s): + x = self.norm1(x, s) + x = self.actv(x) + x = self.pool(x) + x = self.conv1(self.dropout(x)) + x = self.norm2(x, s) + x = self.actv(x) + x = self.conv2(self.dropout(x)) + return x + + def forward(self, x, s): + out = self._residual(x, s) + out = (out + self._shortcut(x)) / np.sqrt(2) + return out + +class AdaLayerNorm(nn.Module): + def __init__(self, style_dim, channels, eps=1e-5): + super().__init__() + self.channels = channels + self.eps = eps + + self.fc = nn.Linear(style_dim, channels*2) + + def forward(self, x, s): + x = x.transpose(-1, -2) + x = x.transpose(1, -1) + + h = self.fc(s) + h = h.view(h.size(0), h.size(1), 1) + gamma, beta = torch.chunk(h, chunks=2, dim=1) + gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1) + + + x = F.layer_norm(x, (self.channels,), eps=self.eps) + x = (1 + gamma) * x + beta + return x.transpose(1, -1).transpose(-1, -2) + +class ProsodyPredictor(nn.Module): + + def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1): + super().__init__() + + self.text_encoder = DurationEncoder(sty_dim=style_dim, + d_model=d_hid, + nlayers=nlayers, + dropout=dropout) + + self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) + self.duration_proj = LinearNorm(d_hid, max_dur) + + self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) + self.F0 = nn.ModuleList() + self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) + self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) + self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) + + self.N = nn.ModuleList() + self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) + self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) + self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) + + self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) + self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) + + + def forward(self, texts, style, text_lengths, alignment, m): + d = self.text_encoder(texts, style, text_lengths, m) + + batch_size = d.shape[0] + text_size = d.shape[1] + + # predict duration + input_lengths = text_lengths.cpu().numpy() + x = nn.utils.rnn.pack_padded_sequence( + d, input_lengths, batch_first=True, enforce_sorted=False) + + m = m.to(text_lengths.device).unsqueeze(1) + + self.lstm.flatten_parameters() + x, _ = self.lstm(x) + x, _ = nn.utils.rnn.pad_packed_sequence( + x, batch_first=True) + + x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]]) + + x_pad[:, :x.shape[1], :] = x + x = x_pad.to(x.device) + + duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training)) + + en = (d.transpose(-1, -2) @ alignment) + + return duration.squeeze(-1), en + + def F0Ntrain(self, x, s): + x, _ = self.shared(x.transpose(-1, -2)) + + F0 = x.transpose(-1, -2) + for block in self.F0: + F0 = block(F0, s) + F0 = self.F0_proj(F0) + + N = x.transpose(-1, -2) + for block in self.N: + N = block(N, s) + N = self.N_proj(N) + + return F0.squeeze(1), N.squeeze(1) + + def length_to_mask(self, lengths): + mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) + mask = torch.gt(mask+1, lengths.unsqueeze(1)) + return mask + +class DurationEncoder(nn.Module): + + def __init__(self, sty_dim, d_model, nlayers, dropout=0.1): + super().__init__() + self.lstms = nn.ModuleList() + for _ in range(nlayers): + self.lstms.append(nn.LSTM(d_model + sty_dim, + d_model // 2, + num_layers=1, + batch_first=True, + bidirectional=True, + dropout=dropout)) + self.lstms.append(AdaLayerNorm(sty_dim, d_model)) + + + self.dropout = dropout + self.d_model = d_model + self.sty_dim = sty_dim + + def forward(self, x, style, text_lengths, m): + masks = m.to(text_lengths.device) + + x = x.permute(2, 0, 1) + s = style.expand(x.shape[0], x.shape[1], -1) + x = torch.cat([x, s], axis=-1) + x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0) + + x = x.transpose(0, 1) + input_lengths = text_lengths.cpu().numpy() + x = x.transpose(-1, -2) + + for block in self.lstms: + if isinstance(block, AdaLayerNorm): + x = block(x.transpose(-1, -2), style).transpose(-1, -2) + x = torch.cat([x, s.permute(1, -1, 0)], axis=1) + x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0) + else: + x = x.transpose(-1, -2) + x = nn.utils.rnn.pack_padded_sequence( + x, input_lengths, batch_first=True, enforce_sorted=False) + block.flatten_parameters() + x, _ = block(x) + x, _ = nn.utils.rnn.pad_packed_sequence( + x, batch_first=True) + x = F.dropout(x, p=self.dropout, training=self.training) + x = x.transpose(-1, -2) + + x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) + + x_pad[:, :, :x.shape[-1]] = x + x = x_pad.to(x.device) + + return x.transpose(-1, -2) + + def inference(self, x, style): + x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model) + style = style.expand(x.shape[0], x.shape[1], -1) + x = torch.cat([x, style], axis=-1) + src = self.pos_encoder(x) + output = self.transformer_encoder(src).transpose(0, 1) + return output + + def length_to_mask(self, lengths): + mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) + mask = torch.gt(mask+1, lengths.unsqueeze(1)) + return mask + +# https://github.com/yl4579/StyleTTS2/blob/main/utils.py +def recursive_munch(d): + if isinstance(d, dict): + return Munch((k, recursive_munch(v)) for k, v in d.items()) + elif isinstance(d, list): + return [recursive_munch(v) for v in d] + else: + return d + +def build_model(path, device): + config = Path(__file__).parent / 'config.json' + assert config.exists(), f'Config path incorrect: config.json not found at {config}' + with open(config, 'r') as r: + args = recursive_munch(json.load(r)) + assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}' + decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels, + resblock_kernel_sizes = args.decoder.resblock_kernel_sizes, + upsample_rates = args.decoder.upsample_rates, + upsample_initial_channel=args.decoder.upsample_initial_channel, + resblock_dilation_sizes=args.decoder.resblock_dilation_sizes, + upsample_kernel_sizes=args.decoder.upsample_kernel_sizes, + gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size) + text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token) + predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout) + bert = load_plbert() + bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim) + for parent in [bert, bert_encoder, predictor, decoder, text_encoder]: + for child in parent.children(): + if isinstance(child, nn.RNNBase): + child.flatten_parameters() + model = Munch( + bert=bert.to(device).eval(), + bert_encoder=bert_encoder.to(device).eval(), + predictor=predictor.to(device).eval(), + decoder=decoder.to(device).eval(), + text_encoder=text_encoder.to(device).eval(), + ) + for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items(): + assert key in model, key + try: + model[key].load_state_dict(state_dict) + except: + state_dict = {k[7:]: v for k, v in state_dict.items()} + model[key].load_state_dict(state_dict, strict=False) + return model diff --git a/output.wav b/output.wav new file mode 100644 index 0000000000000000000000000000000000000000..87c1a104bfe3ba3a3e5b57c3290fc7b0509671d5 --- /dev/null +++ b/output.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92965112db3609251ee1e91c47a103e14c30e274fffada46ef78934ba64d9784 +size 1406444 diff --git a/plbert.py b/plbert.py new file mode 100644 index 0000000000000000000000000000000000000000..ef54f57bb8405abebfcd052bcb2be1249ce510bc --- /dev/null +++ b/plbert.py @@ -0,0 +1,15 @@ +# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py +from transformers import AlbertConfig, AlbertModel + +class CustomAlbert(AlbertModel): + def forward(self, *args, **kwargs): + # Call the original forward method + outputs = super().forward(*args, **kwargs) + # Only return the last_hidden_state + return outputs.last_hidden_state + +def load_plbert(): + plbert_config = {'vocab_size': 178, 'hidden_size': 768, 'num_attention_heads': 12, 'intermediate_size': 2048, 'max_position_embeddings': 512, 'num_hidden_layers': 12, 'dropout': 0.1} + albert_base_configuration = AlbertConfig(**plbert_config) + bert = CustomAlbert(albert_base_configuration) + return bert diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c2a9df8e7a5cecd49ffb95e11cd6c6c29c83b94 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +phonemizer==3.2.1 +transformers==4.30.0 +scipy==1.10.1 +munch==2.5.0 +python-dotenv==1.0.0 +openai==0.27.2 +grpcio==1.56.0 +grpcio-tools==1.56.0 +torch>=2.0.0 +torchvision>=0.15.0 +torchaudio>=2.0.0 \ No newline at end of file diff --git a/text_to_speech.proto b/text_to_speech.proto new file mode 100644 index 0000000000000000000000000000000000000000..e9c2b94bd00b9520192f5eace77ac29970f7ee5e --- /dev/null +++ b/text_to_speech.proto @@ -0,0 +1,32 @@ +syntax = "proto3"; + +package texttospeech; + +service TextToSpeechService { + rpc ProcessText (stream ProcessTextRequest) returns (stream ProcessTextResponse); +} + +message ProcessTextRequest { + oneof request_data { + string text = 1; + Meta metadata = 2; + Status status = 3; + } +} + +message ProcessTextResponse { + bytes buffer = 1; + string session_id = 2; + string sequence_id = 3; + string transcript = 4; +} + +message Meta { + string session_id = 1; +} + +message Status { + string transcript = 1; + string played_seq = 2; + string interrupt_seq = 3; +} \ No newline at end of file diff --git a/text_to_speech_pb2.py b/text_to_speech_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..940a254d329b9855ba73c47df7d1cfa2eeefda82 --- /dev/null +++ b/text_to_speech_pb2.py @@ -0,0 +1,44 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: text_to_speech.proto +# Protobuf Python Version: 5.29.0 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 29, + 0, + '', + 'text_to_speech.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14text_to_speech.proto\x12\x0ctexttospeech\"\x84\x01\n\x12ProcessTextRequest\x12\x0e\n\x04text\x18\x01 \x01(\tH\x00\x12&\n\x08metadata\x18\x02 \x01(\x0b\x32\x12.texttospeech.MetaH\x00\x12&\n\x06status\x18\x03 \x01(\x0b\x32\x14.texttospeech.StatusH\x00\x42\x0e\n\x0crequest_data\"b\n\x13ProcessTextResponse\x12\x0e\n\x06\x62uffer\x18\x01 \x01(\x0c\x12\x12\n\nsession_id\x18\x02 \x01(\t\x12\x13\n\x0bsequence_id\x18\x03 \x01(\t\x12\x12\n\ntranscript\x18\x04 \x01(\t\"\x1a\n\x04Meta\x12\x12\n\nsession_id\x18\x01 \x01(\t\"G\n\x06Status\x12\x12\n\ntranscript\x18\x01 \x01(\t\x12\x12\n\nplayed_seq\x18\x02 \x01(\t\x12\x15\n\rinterrupt_seq\x18\x03 \x01(\t2m\n\x13TextToSpeechService\x12V\n\x0bProcessText\x12 .texttospeech.ProcessTextRequest\x1a!.texttospeech.ProcessTextResponse(\x01\x30\x01\x62\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'text_to_speech_pb2', _globals) +if not _descriptor._USE_C_DESCRIPTORS: + DESCRIPTOR._loaded_options = None + _globals['_PROCESSTEXTREQUEST']._serialized_start=39 + _globals['_PROCESSTEXTREQUEST']._serialized_end=171 + _globals['_PROCESSTEXTRESPONSE']._serialized_start=173 + _globals['_PROCESSTEXTRESPONSE']._serialized_end=271 + _globals['_META']._serialized_start=273 + _globals['_META']._serialized_end=299 + _globals['_STATUS']._serialized_start=301 + _globals['_STATUS']._serialized_end=372 + _globals['_TEXTTOSPEECHSERVICE']._serialized_start=374 + _globals['_TEXTTOSPEECHSERVICE']._serialized_end=483 +# @@protoc_insertion_point(module_scope) diff --git a/text_to_speech_pb2_grpc.py b/text_to_speech_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..c4154f6b120d394a103a02bf9e5ac03acee3938c --- /dev/null +++ b/text_to_speech_pb2_grpc.py @@ -0,0 +1,97 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + +import text_to_speech_pb2 as text__to__speech__pb2 + +GRPC_GENERATED_VERSION = '1.69.0' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in text_to_speech_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) + + +class TextToSpeechServiceStub(object): + """Missing associated documentation comment in .proto file.""" + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.ProcessText = channel.stream_stream( + '/texttospeech.TextToSpeechService/ProcessText', + request_serializer=text__to__speech__pb2.ProcessTextRequest.SerializeToString, + response_deserializer=text__to__speech__pb2.ProcessTextResponse.FromString, + _registered_method=True) + + +class TextToSpeechServiceServicer(object): + """Missing associated documentation comment in .proto file.""" + + def ProcessText(self, request_iterator, context): + """Missing associated documentation comment in .proto file.""" + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_TextToSpeechServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'ProcessText': grpc.stream_stream_rpc_method_handler( + servicer.ProcessText, + request_deserializer=text__to__speech__pb2.ProcessTextRequest.FromString, + response_serializer=text__to__speech__pb2.ProcessTextResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'texttospeech.TextToSpeechService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + server.add_registered_method_handlers('texttospeech.TextToSpeechService', rpc_method_handlers) + + + # This class is part of an EXPERIMENTAL API. +class TextToSpeechService(object): + """Missing associated documentation comment in .proto file.""" + + @staticmethod + def ProcessText(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/texttospeech.TextToSpeechService/ProcessText', + text__to__speech__pb2.ProcessTextRequest.SerializeToString, + text__to__speech__pb2.ProcessTextResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) diff --git a/voices/af.pt b/voices/af.pt new file mode 100644 index 0000000000000000000000000000000000000000..2e4bdbbe54f437d17c668b5f64faa16746759b88 --- /dev/null +++ b/voices/af.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fad4192fd8a840f925b0e3fc2be54e20531f91a9ac816a485b7992ca0bd83ebf +size 524355 diff --git a/voices/af_bella.pt b/voices/af_bella.pt new file mode 100644 index 0000000000000000000000000000000000000000..0894c4dfa49b492b88026353659b9658e61c9218 --- /dev/null +++ b/voices/af_bella.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2828c6c2f94275ef3441a2edfcf48293298ee0f9b56ce70fb2e344345487b922 +size 524449 diff --git a/voices/af_nicole.pt b/voices/af_nicole.pt new file mode 100644 index 0000000000000000000000000000000000000000..e77e41c12c543ca0ae131801cfd194f45006032e --- /dev/null +++ b/voices/af_nicole.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9401802fb0b7080c324dec1a75d60f31d977ced600a99160e095dbc5a1172692 +size 524454 diff --git a/voices/af_sarah.pt b/voices/af_sarah.pt new file mode 100644 index 0000000000000000000000000000000000000000..ce3327f86256b40ab606671354391c77df21aba5 --- /dev/null +++ b/voices/af_sarah.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba7918c4ace6ace4221e7e01eb3a6d16596cba9729850551c758cd2ad3a4cd08 +size 524449 diff --git a/voices/af_sky.pt b/voices/af_sky.pt new file mode 100644 index 0000000000000000000000000000000000000000..bf178060e7eaf614f93eee00534c20a0f948fbea --- /dev/null +++ b/voices/af_sky.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f16f1bb778de36a177ae4b0b6f1e59783d5f4d3bcecf752c3e1ee98299b335e +size 524375 diff --git a/voices/am_adam.pt b/voices/am_adam.pt new file mode 100644 index 0000000000000000000000000000000000000000..1e812869b73ec2ebb4fec9893aeac470cf5e2e5a --- /dev/null +++ b/voices/am_adam.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1921528b400a553f66528c27899d95780918fe33b1ac7e2a871f6a0de475f176 +size 524444 diff --git a/voices/am_michael.pt b/voices/am_michael.pt new file mode 100644 index 0000000000000000000000000000000000000000..7acd7effc21d420948789cf186dee9ffb58ec557 --- /dev/null +++ b/voices/am_michael.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a255c9562c363103adc56c09b7daf837139d3bdaa8bd4dd74847ab1e3e8c28be +size 524459 diff --git a/voices/bf_emma.pt b/voices/bf_emma.pt new file mode 100644 index 0000000000000000000000000000000000000000..012bef4d3425276e0e68a44c2ee86af7635b9eaa --- /dev/null +++ b/voices/bf_emma.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:992e6d8491b8926ef4a16205250e51a21d9924405a5d37e2db6e94adfd965c3b +size 524365 diff --git a/voices/bf_isabella.pt b/voices/bf_isabella.pt new file mode 100644 index 0000000000000000000000000000000000000000..67f0df78f0ab2dd1d9fd295284036d122d09b691 --- /dev/null +++ b/voices/bf_isabella.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0865a03931230100167f7a81d394b143c072efe2d7e4c4a87b5c54d6283f580 +size 524365 diff --git a/voices/bm_george.pt b/voices/bm_george.pt new file mode 100644 index 0000000000000000000000000000000000000000..5e058933596c8a0e6d125a08c3d26db547929d0c --- /dev/null +++ b/voices/bm_george.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d763dfe13e934357f4d8322b718787d79e32f2181e29ca0cf6aa637d8092b96 +size 524464 diff --git a/voices/bm_lewis.pt b/voices/bm_lewis.pt new file mode 100644 index 0000000000000000000000000000000000000000..ab94dac15af9ee3a67ef63629205a3b364629f75 --- /dev/null +++ b/voices/bm_lewis.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f70d9ea4d65f522f224628f06d86ea74279faae23bd7e765848a374aba916b76 +size 524449