app.py

import gradio as gr
from gradio_webrtc import WebRTC, AdditionalOutputs, ReplyOnPause
from pydub import AudioSegment
from io import BytesIO
import numpy as np
import librosa
import tempfile
from twilio.rest import Client
import os
import spaces
import uuid
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
import logging

# Configure the root logger to WARNING to suppress debug messages from other libraries
logging.basicConfig(level=logging.WARNING)

# Create a console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)

# Create a formatter
formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s")
console_handler.setFormatter(formatter)

# Configure the logger for your specific library
logger = logging.getLogger("gradio_webrtc")
logger.setLevel(logging.DEBUG)
logger.addHandler(console_handler)


processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct", device_map="auto")

account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
auth_token = os.environ.get("TWILIO_AUTH_TOKEN")

if account_sid and auth_token:
    client = Client(account_sid, auth_token)

    token = client.tokens.create()

    rtc_configuration = {
        "iceServers": token.ice_servers,
        "iceTransportPolicy": "relay",
    }
else:
    rtc_configuration = None


@spaces.GPU
def transcribe(audio: tuple[int, np.ndarray], transformers_convo: list[dict], gradio_convo: list[dict]):
    segment = AudioSegment(audio[1].tobytes(), frame_rate=audio[0], sample_width=audio[1].dtype.itemsize, channels=1)

    name = str(uuid.uuid4()) + ".mp3"
    segment.export(name, format="mp3")
    transformers_convo.append({"role": "user", "content": [{"type": "audio", "audio_url": name}]})
    gradio_convo.append({"role": "assistant", "content": gr.Audio(value=name)})
    text = processor.apply_chat_template(transformers_convo, add_generation_prompt=True, tokenize=False)
    audios = []
    for message in transformers_convo:
        if isinstance(message["content"], list):
            for ele in message["content"]:
                if ele["type"] == "audio":
                    audios.append(librosa.load(
                        BytesIO(open(ele['audio_url'], "rb").read()), 
                        sr=processor.feature_extractor.sampling_rate)[0]
                    )
    inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
    inputs = dict(**inputs)
    inputs["input_ids"] = inputs["input_ids"].to("cuda:0")

    generate_ids = model.generate(**inputs, max_length=256)
    generate_ids = generate_ids[:, inputs["input_ids"].size(1):]
    response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    print("response", response)
    transformers_convo.append({"role": "assistant", "content": response})
    gradio_convo.append({"role": "assistant", "content": response})

    yield AdditionalOutputs(transformers_convo, gradio_convo)


with gr.Blocks() as demo:
    transformers_convo = gr.State(value=[])
    with gr.Row():
        with gr.Column():
            audio = WebRTC(
                rtc_configuration=rtc_configuration,
                label="Stream",
                mode="send",
                modality="audio",
            )
        with gr.Column():
            transcript = gr.Chatbot(label="transcript", type="messages")

    audio.stream(ReplyOnPause(transcribe), inputs=[audio, transformers_convo, transcript], outputs=[audio])
    audio.on_additional_outputs(lambda s,a: (s,a), outputs=[transformers_convo, transcript])

if __name__ == "__main__":
    demo.launch()