speaches/main.py

from __future__ import annotations

import asyncio
import logging
import time
from contextlib import asynccontextmanager
from io import BytesIO
from typing import Annotated

from fastapi import (
    Depends,
    FastAPI,
    Response,
    UploadFile,
    WebSocket,
    WebSocketDisconnect,
)
from fastapi.websockets import WebSocketState
from faster_whisper import WhisperModel
from faster_whisper.vad import VadOptions, get_speech_timestamps

from speaches.asr import FasterWhisperASR, TranscribeOpts
from speaches.audio import AudioStream, audio_samples_from_file
from speaches.config import SAMPLES_PER_SECOND, Language, config
from speaches.core import Transcription
from speaches.logger import logger
from speaches.server_models import (
    ResponseFormat,
    TranscriptionResponse,
    TranscriptionVerboseResponse,
)
from speaches.transcriber import audio_transcriber

whisper: WhisperModel = None  # type: ignore


@asynccontextmanager
async def lifespan(_: FastAPI):
    global whisper
    logging.debug(f"Loading {config.whisper.model}")
    start = time.perf_counter()
    whisper = WhisperModel(
        config.whisper.model,
        device=config.whisper.inference_device,
        compute_type=config.whisper.compute_type,
    )
    end = time.perf_counter()
    logger.debug(f"Loaded {config.whisper.model} loaded in {end - start:.2f} seconds")
    yield


app = FastAPI(lifespan=lifespan)


@app.get("/health")
def health() -> Response:
    return Response(status_code=200, content="Everything is peachy!")


async def transcription_parameters(
    language: Language = Language.EN,
    vad_filter: bool = True,
    condition_on_previous_text: bool = False,
) -> TranscribeOpts:
    return TranscribeOpts(
        language=language,
        vad_filter=vad_filter,
        condition_on_previous_text=condition_on_previous_text,
    )


TranscribeParams = Annotated[TranscribeOpts, Depends(transcription_parameters)]


@app.post("/v1/audio/transcriptions")
async def transcribe_file(
    file: UploadFile,
    transcription_opts: TranscribeParams,
    response_format: ResponseFormat = ResponseFormat.JSON,
) -> str:
    asr = FasterWhisperASR(whisper, transcription_opts)
    audio_samples = audio_samples_from_file(file.file)
    audio = AudioStream(audio_samples)
    transcription, _ = await asr.transcribe(audio)
    return format_transcription(transcription, response_format)


async def audio_receiver(ws: WebSocket, audio_stream: AudioStream) -> None:
    try:
        while True:
            bytes_ = await asyncio.wait_for(
                ws.receive_bytes(), timeout=config.max_no_data_seconds
            )
            logger.debug(f"Received {len(bytes_)} bytes of audio data")
            audio_samples = audio_samples_from_file(BytesIO(bytes_))
            audio_stream.extend(audio_samples)
            if audio_stream.duration - config.inactivity_window_seconds >= 0:
                audio = audio_stream.after(
                    audio_stream.duration - config.inactivity_window_seconds
                )
                vad_opts = VadOptions(min_silence_duration_ms=500, speech_pad_ms=0)
                timestamps = get_speech_timestamps(audio.data, vad_opts)
                if len(timestamps) == 0:
                    logger.info(
                        f"No speech detected in the last {config.inactivity_window_seconds} seconds."
                    )
                    break
                elif (
                    # last speech end time
                    config.inactivity_window_seconds
                    - timestamps[-1]["end"] / SAMPLES_PER_SECOND
                    >= config.max_inactivity_seconds
                ):
                    logger.info(
                        f"Not enough speech in the last {config.inactivity_window_seconds} seconds."
                    )
                    break
    except asyncio.TimeoutError:
        logger.info(
            f"No data received in {config.max_no_data_seconds} seconds. Closing the connection."
        )
    except WebSocketDisconnect as e:
        logger.info(f"Client disconnected: {e}")
    audio_stream.close()


def format_transcription(
    transcription: Transcription, response_format: ResponseFormat
) -> str:
    if response_format == ResponseFormat.TEXT:
        return transcription.text
    elif response_format == ResponseFormat.JSON:
        return TranscriptionResponse(text=transcription.text).model_dump_json()
    elif response_format == ResponseFormat.VERBOSE_JSON:
        return TranscriptionVerboseResponse(
            duration=transcription.duration,
            text=transcription.text,
            words=transcription.words,
        ).model_dump_json()


@app.websocket("/v1/audio/transcriptions")
async def transcribe_stream(
    ws: WebSocket,
    transcription_opts: TranscribeParams,
    response_format: ResponseFormat = ResponseFormat.JSON,
) -> None:
    await ws.accept()
    asr = FasterWhisperASR(whisper, transcription_opts)
    audio_stream = AudioStream()
    async with asyncio.TaskGroup() as tg:
        tg.create_task(audio_receiver(ws, audio_stream))
        async for transcription in audio_transcriber(asr, audio_stream):
            logger.debug(f"Sending transcription: {transcription.text}")
            # Or should it be
            if ws.client_state == WebSocketState.DISCONNECTED:
                break
            await ws.send_text(format_transcription(transcription, response_format))

    if not ws.client_state == WebSocketState.DISCONNECTED:
        # this means that the client HASNT disconnected
        await ws.close()