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import os
import time
import gradio as gr
import numpy as np
from dotenv import load_dotenv
from elevenlabs import ElevenLabs
from fastrtc import (
Stream,
get_stt_model,
ReplyOnPause,
AdditionalOutputs
)
import requests
import io
import soundfile as sf
from gtts import gTTS
import re
import inspect
from deepseek import DeepSeekAPI
# Load environment variables
load_dotenv()
# Initialize clients
elevenlabs_client = ElevenLabs(api_key=os.getenv("ELEVENLABS_API_KEY"))
stt_model = get_stt_model()
deepseek_client = DeepSeekAPI(api_key=os.getenv("DEEPSEEK_API_KEY"))
# Add this debug code temporarily to see what methods are available:
print(dir(deepseek_client))
def response(
audio: tuple[int, np.ndarray],
chatbot: list[dict] | None = None,
):
chatbot = chatbot or []
messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
# Convert speech to text
text = stt_model.stt(audio)
print("prompt:", text)
# Add user message to chat
chatbot.append({"role": "user", "content": text})
yield AdditionalOutputs(chatbot)
# Get AI response
messages.append({"role": "user", "content": text})
response_text = get_deepseek_response(messages)
# Add AI response to chat
chatbot.append({"role": "assistant", "content": response_text})
# Convert response to speech
for audio_data in text_to_speech(response_text):
if audio_data:
yield audio_data
yield AdditionalOutputs(chatbot)
# Create Gradio interface
chatbot = gr.Chatbot(type="messages")
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response, input_sample_rate=16000),
additional_outputs_handler=lambda a, b: b,
additional_inputs=[chatbot],
additional_outputs=[chatbot],
ui_args={"title": "LLM Voice Chat (Powered by DeepSeek & ElevenLabs)"}
)
# Create FastAPI app and mount stream
from fastapi import FastAPI
app = FastAPI()
app = gr.mount_gradio_app(app, stream.ui, path="/")
stream.mount(app) # Mount the stream for telephone/fastphone integration
# Update the chat completion part based on available methods:
# We'll use direct HTTP requests as a fallback since the API structure is unclear:
def get_deepseek_response(messages):
url = "https://api.deepseek.com/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('DEEPSEEK_API_KEY')}"
}
payload = {
"model": "deepseek-chat",
"messages": messages,
"temperature": 0.7,
"max_tokens": 512
}
response = requests.post(url, json=payload, headers=headers)
# Check for error response
if response.status_code != 200:
print(f"DeepSeek API error: {response.status_code} - {response.text}")
return "I'm sorry, I encountered an error processing your request."
response_json = response.json()
return response_json["choices"][0]["message"]["content"]
# Make sure that the text_to_speech function is completely replaced and gTTS is explicitly using US English
def text_to_speech(text):
"""Convert text to speech using Google TTS with sentence-by-sentence processing"""
try:
# Split text into sentences for faster perceived response
sentences = re.split(r'(?<=[.!?])\s+', text)
for sentence in sentences:
if not sentence.strip():
continue
# Process each sentence separately
mp3_fp = io.BytesIO()
# Force US English - be explicit about it
print(f"Using gTTS with en-us locale for sentence: {sentence[:20]}...")
tts = gTTS(text=sentence, lang='en-us', tld='com', slow=False)
tts.write_to_fp(mp3_fp)
mp3_fp.seek(0)
# Process audio data
data, samplerate = sf.read(mp3_fp)
# Convert to mono if stereo
if len(data.shape) > 1 and data.shape[1] > 1:
data = data[:, 0]
# Resample to 24000 Hz if needed
if samplerate != 24000:
data = np.interp(
np.linspace(0, len(data), int(len(data) * 24000 / samplerate)),
np.arange(len(data)),
data
)
# Convert to 16-bit integers
data = (data * 32767).astype(np.int16)
# Ensure buffer size is even
if len(data) % 2 != 0:
data = np.append(data, [0])
# Reshape and yield in chunks
chunk_size = 4800
for i in range(0, len(data), chunk_size):
chunk = data[i:i+chunk_size]
if len(chunk) > 0:
if len(chunk) % 2 != 0:
chunk = np.append(chunk, [0])
chunk = chunk.reshape(1, -1)
yield (24000, chunk)
except Exception as e:
print(f"Exception in text_to_speech: {e}")
yield None
# Add this debug statement AFTER the function definition
print("text_to_speech function:", inspect.getsource(text_to_speech))
if __name__ == "__main__":
os.environ["GRADIO_SSR_MODE"] = "false"
# Check FastRTC version
import fastrtc
print(f"FastRTC version: {fastrtc.__version__ if hasattr(fastrtc, '__version__') else 'unknown'}")
# Try running fastphone with additional diagnostic
print("Starting phone service - attempting to inspect fastphone method...")
import inspect
print(f"FastPhone signature: {inspect.signature(stream.fastphone) if hasattr(stream, 'fastphone') else 'Not available'}")
try:
# Fix: Use keyword argument instead of positional
phone_service = stream.fastphone(
token=os.getenv("HF_TOKEN"),
host="127.0.0.1",
port=8000,
share_server_tls_certificate=True # Use keyword argument format
)
print("Phone service started successfully")
except Exception as e:
print(f"Error starting phone service: {e}")
print("Falling back to web interface...")
# Launch with web interface as fallback
stream.ui.launch(server_port=7860)
# Remove or comment out the following lines:
# !pip install -q torch==2.0.1 torchaudio==2.0.2 gradio requests soundfile huggingface_hub
# !wget -q https://github.com/seasalt-ai/csm/archive/refs/heads/main.zip
# !unzip -q main.zip
# !mv csm-main csm
# !cd csm && pip install -e .
#
# # Set up directories
# import os
# import sys
# sys.path.append("/content/csm")
# voice_samples_dir = "/content/csm_voice_samples"
# output_dir = "/content/csm_output"
# os.makedirs(voice_samples_dir, exist_ok=True)
# os.makedirs(output_dir, exist_ok=True)
#
# print("✅ Dependencies installed!") |