app.py

import base64
import io
import os
import tempfile
import time
import traceback
from dataclasses import dataclass
from queue import Queue
from threading import Thread

import gradio as gr
import librosa
import numpy as np
import requests
from gradio_webrtc import StreamHandler, WebRTC
from huggingface_hub import snapshot_download
from pydub import AudioSegment
from twilio.rest import Client

from server import serve

# from server import serve
from utils.vad import VadOptions, collect_chunks, get_speech_timestamps

repo_id = "gpt-omni/mini-omni"
snapshot_download(repo_id, local_dir="./checkpoint", revision="main")

IP = "0.0.0.0"
PORT = 60808

thread = Thread(target=serve, daemon=True)
thread.start()


API_URL = "http://0.0.0.0:60808/chat"

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

# recording parameters
IN_CHANNELS = 1
IN_RATE = 24000
IN_CHUNK = 1024
IN_SAMPLE_WIDTH = 2
VAD_STRIDE = 0.5

# playing parameters
OUT_CHANNELS = 1
OUT_RATE = 24000
OUT_SAMPLE_WIDTH = 2
OUT_CHUNK = 20 * 4096


def run_vad(ori_audio, sr):
    _st = time.time()
    try:
        audio = ori_audio
        audio = audio.astype(np.float32) / 32768.0
        sampling_rate = 16000
        if sr != sampling_rate:
            audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate)

        vad_parameters = {}
        vad_parameters = VadOptions(**vad_parameters)
        speech_chunks = get_speech_timestamps(audio, vad_parameters)
        audio = collect_chunks(audio, speech_chunks)
        duration_after_vad = audio.shape[0] / sampling_rate

        if sr != sampling_rate:
            # resample to original sampling rate
            vad_audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=sr)
        else:
            vad_audio = audio
        vad_audio = np.round(vad_audio * 32768.0).astype(np.int16)
        vad_audio_bytes = vad_audio.tobytes()

        return duration_after_vad, vad_audio_bytes, round(time.time() - _st, 4)
    except Exception as e:
        msg = f"[asr vad error] audio_len: {len(ori_audio)/(sr*2):.3f} s, trace: {traceback.format_exc()}"
        print(msg)
        return -1, ori_audio, round(time.time() - _st, 4)


def warm_up():
    frames = np.zeros((1, 1600))  # 1024 frames of 2 bytes each
    _, frames, tcost = run_vad(frames, 16000)
    print(f"warm up done, time_cost: {tcost:.3f} s")


# warm_up()


@dataclass
class AppState:
    stream: np.ndarray | None = None
    sampling_rate: int = 0
    pause_detected: bool = False
    started_talking: bool = False
    responding: bool = False
    stopped: bool = False
    buffer: np.ndarray | None = None


def determine_pause(audio: np.ndarray, sampling_rate: int, state: AppState) -> bool:
    """Take in the stream, determine if a pause happened"""
    duration = len(audio) / sampling_rate

    dur_vad, _, _ = run_vad(audio, sampling_rate)

    if duration >= 0.60:
        if dur_vad > 0.2 and not state.started_talking:
            print("started talking")
            state.started_talking = True
        if state.started_talking:
            if state.stream is None:
                state.stream = audio
            else:
                state.stream = np.concatenate((state.stream, audio))
        state.buffer = None
        if dur_vad < 0.1 and state.started_talking:
            segment = AudioSegment(
                state.stream.tobytes(),
                frame_rate=sampling_rate,
                sample_width=audio.dtype.itemsize,
                channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),
            )

            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
                segment.export(f.name, format="wav")
            print("input file written", f.name)
            return True
    return False


def speaking(audio_bytes: str):
    base64_encoded = str(base64.b64encode(audio_bytes), encoding="utf-8")
    files = {"audio": base64_encoded}
    byte_buffer = b""
    with requests.post(API_URL, json=files, stream=True) as response:
        try:
            for chunk in response.iter_content(chunk_size=OUT_CHUNK):
                if chunk:
                    # Create an audio segment from the numpy array
                    byte_buffer += chunk
                    audio_segment = AudioSegment(
                        chunk + b"\x00" if len(chunk) % 2 != 0 else chunk,
                        frame_rate=OUT_RATE,
                        sample_width=OUT_SAMPLE_WIDTH,
                        channels=OUT_CHANNELS,
                    )
                    # Export the audio segment to a numpy array
                    audio_np = np.array(audio_segment.get_array_of_samples())
                    yield audio_np.reshape(1, -1)
            all_output_audio = AudioSegment(
                byte_buffer,
                frame_rate=OUT_RATE,
                sample_width=OUT_SAMPLE_WIDTH,
                channels=1,
            )
            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
                all_output_audio.export(f.name, format="wav")
                print("output file written", f.name)
        except Exception as e:
            raise gr.Error(f"Error during audio streaming: {e}")


def process_audio(audio: tuple, state: AppState) -> None:
    frame_rate, array = audio
    array = np.squeeze(array)
    if not state.sampling_rate:
        state.sampling_rate = frame_rate
    if state.buffer is None:
        state.buffer = array
    else:
        state.buffer = np.concatenate((state.buffer, array))

    pause_detected = determine_pause(state.buffer, state.sampling_rate, state)
    state.pause_detected = pause_detected


def response(state: AppState):
    if not state.pause_detected and not state.started_talking:
        return None

    audio_buffer = io.BytesIO()
    segment = AudioSegment(
        state.stream.tobytes(),
        frame_rate=state.sampling_rate,
        sample_width=state.stream.dtype.itemsize,
        channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),
    )
    segment.export(audio_buffer, format="wav")

    for numpy_array in speaking(audio_buffer.getvalue()):
        yield (OUT_RATE, numpy_array, "mono")


class OmniHandler(StreamHandler):
    def __init__(self) -> None:
        super().__init__(
            expected_layout="mono", output_sample_rate=OUT_RATE, output_frame_size=480
        )
        self.chunk_queue = Queue()
        self.state = AppState()
        self.generator = None
        self.duration = 0

    def receive(self, frame: tuple[int, np.ndarray]) -> None:
        if self.state.responding:
            return
        process_audio(frame, self.state)
        if self.state.pause_detected:
            self.chunk_queue.put(True)

    def reset(self):
        self.generator = None
        self.state = AppState()
        self.duration = 0

    def emit(self):
        if not self.generator:
            self.chunk_queue.get()
            self.state.responding = True
            self.generator = response(self.state)
        try:
            return next(self.generator)
        except StopIteration:
            self.reset()


with gr.Blocks() as demo:
    gr.HTML(
        """
    <h1 style='text-align: center'>
    Omni Chat (Powered by WebRTC ⚡️)
    </h1>
    """
    )
    with gr.Column():
        with gr.Group():
            audio = WebRTC(
                label="Stream",
                rtc_configuration=rtc_configuration,
                mode="send-receive",
                modality="audio",
            )
        audio.stream(fn=OmniHandler(), inputs=[audio], outputs=[audio], time_limit=300)


demo.launch()