whisper_fastapi_online_server.py

import io
import requests
import argparse
import asyncio
import numpy as np
import ffmpeg
from time import time

from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware

from src.whisper_streaming.whisper_online import backend_factory, online_factory, add_shared_args


import logging
import logging.config
from transformers import pipeline

MODEL_NAME = 'Helsinki-NLP/opus-tatoeba-en-ja'
TRANSLATOR = pipeline('translation', model=MODEL_NAME, device='cuda')
TRANSLATOR('Warming up!')

API_KEY = '3c2b8b0f-4fa9-4eb7-b67d-7cae25546051:fx' # 自身の API キーを指定

SOURCE_LANG = 'EN'
TARGET_LANG = 'JA'

def translator_wrapper(source_text, mode='deepl'):
    if mode == 'deepl':
        params = {
                    'auth_key' : API_KEY,
                    'text' : source_text,
                    'source_lang' : SOURCE_LANG, # 翻訳対象の言語
                    "target_lang": TARGET_LANG  # 翻訳後の言語
                }

        # リクエストを投げる
        try:
            request = requests.post("https://api-free.deepl.com/v2/translate", data=params, timeout=5) # URIは有償版, 無償版で異なるため要注意
            result = request.json()['translations'][0]['text']
        except requests.exceptions.Timeout:
            result = "(timed out)"
        return result

    elif mode == 'marianmt':
        return TRANSLATOR(source_text)[0]['translation_text']

    elif mode == 'google':
        import  requests

        # https://www.eyoucms.com/news/ziliao/other/29445.html
        language_type = ""
        target = 'ja-jp'
        url = "https://translation.googleapis.com/language/translate/v2"
        data = {
            'key':"AIzaSyCX0-Wdxl_rgvcZzklNjnqJ1W9YiKjcHUs", #  認証の設定:APIキー
            'source': language_type,
            'target': target,
            'q': source_text,
            'format': "text"
        }
        #headers = {'X-HTTP-Method-Override': 'GET'}
        #response = requests.post(url, data=data, headers=headers)
        response = requests.post(url, data)
        # print(response.json())
        print(response)
        res = response.json()
        print(res["data"]["translations"][0]["translatedText"])
        result = res["data"]["translations"][0]["translatedText"]
        print(result)
        return result


def setup_logging():
    logging_config = {
        'version': 1,
        'disable_existing_loggers': False,
        'formatters': {
            'standard': {
                'format': '%(asctime)s %(levelname)s [%(name)s]: %(message)s',
            },
        },
        'handlers': {
            'console': {
                'level': 'INFO',
                'class': 'logging.StreamHandler',
                'formatter': 'standard',
            },
        },
        'root': {
            'handlers': ['console'],
            'level': 'DEBUG',
        },
        'loggers': {
            'uvicorn': {
                'handlers': ['console'],
                'level': 'INFO',
                'propagate': False,
            },
            'uvicorn.error': {
                'level': 'INFO',
            },
            'uvicorn.access': {
                'level': 'INFO',
            },
            'src.whisper_streaming.online_asr': {  # Add your specific module here
                'handlers': ['console'],
                'level': 'DEBUG',
                'propagate': False,
            },
            'src.whisper_streaming.whisper_streaming': {  # Add your specific module here
                'handlers': ['console'],
                'level': 'DEBUG',
                'propagate': False,
            },
        },
    }

    logging.config.dictConfig(logging_config)

setup_logging()
logger = logging.getLogger(__name__)






app = FastAPI()
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
parser.add_argument(
    "--host",
    type=str,
    default="localhost",
    help="The host address to bind the server to.",
)
parser.add_argument(
    "--port", type=int, default=8000, help="The port number to bind the server to."
)
parser.add_argument(
    "--warmup-file",
    type=str,
    dest="warmup_file",
    help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .",
)

parser.add_argument(
    "--diarization",
    type=bool,
    default=False,
    help="Whether to enable speaker diarization.",
)


add_shared_args(parser)
args = parser.parse_args()
# args.model = 'medium'

asr, tokenizer = backend_factory(args)

if args.diarization:
    from src.diarization.diarization_online import DiartDiarization


# Load demo HTML for the root endpoint
with open("src/web/live_transcription.html", "r", encoding="utf-8") as f:
    html = f.read()


@app.get("/")
async def get():
    return HTMLResponse(html)


SAMPLE_RATE = 16000
CHANNELS = 1
SAMPLES_PER_SEC = SAMPLE_RATE * int(args.min_chunk_size)
BYTES_PER_SAMPLE = 2  # s16le = 2 bytes per sample
BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE


async def start_ffmpeg_decoder():
    """
    Start an FFmpeg process in async streaming mode that reads WebM from stdin
    and outputs raw s16le PCM on stdout. Returns the process object.
    """
    process = (
        ffmpeg.input("pipe:0", format="webm")
        .output(
            "pipe:1",
            format="s16le",
            acodec="pcm_s16le",
            ac=CHANNELS,
            ar=str(SAMPLE_RATE),
        )
        .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True)
    )
    return process



@app.websocket("/asr")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    print("WebSocket connection opened.")

    ffmpeg_process = await start_ffmpeg_decoder()
    pcm_buffer = bytearray()
    print("Loading online.")
    online = online_factory(args, asr, tokenizer)
    print("Online loaded.")

    if args.diarization:
        diarization = DiartDiarization(SAMPLE_RATE)

    # Continuously read decoded PCM from ffmpeg stdout in a background task
    async def ffmpeg_stdout_reader():
        nonlocal pcm_buffer
        loop = asyncio.get_event_loop()
        full_transcription = ""
        beg = time()
        
        chunk_history = []  # Will store dicts: {beg, end, text, speaker}

        buffers = [{'speaker': '0', 'text': '', 'translation': None}]
        buffer_line = ''
        
        while True:
            print('in while')
            try:
                print('try in while')
                elapsed_time = int(time() - beg)
                beg = time()
                print('before await loop.run_in_executor()')
                chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 32000 * elapsed_time)

                print('before if not chunk')
                if not chunk:  # The first chunk will be almost empty, FFmpeg is still starting up
                    chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 4096)
                    if not chunk:  # FFmpeg might have closed
                        print("FFmpeg stdout closed.")
                        break

                pcm_buffer.extend(chunk)

                print('before if len(pcm_buffer)')
                if len(pcm_buffer) >= BYTES_PER_SEC:
                    print('in if len(pcm_buffer)')
                    # Convert int16 -> float32
                    pcm_array = (np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0)
                    pcm_buffer = bytearray()  # Initialize the PCM buffer
                    online.insert_audio_chunk(pcm_array)
                    beg_trans, end_trans, trans = online.process_iter()
                    
                    if trans:
                        chunk_history.append({
                        "beg": beg_trans,
                        "end": end_trans,
                        "text": trans,
                        "speaker": "0"
                        })
                    full_transcription += trans
                    
                    # ----------------
                    # Process buffer
                    # ----------------
                    if args.vac:
                        # We need to access the underlying online object to get the buffer
                        buffer = online.online.concatenate_tsw(online.online.transcript_buffer.buffer)[2]
                    else:
                        buffer = online.concatenate_tsw(online.transcript_buffer.buffer)[2]

                    if buffer in full_transcription:  # With VAC, the buffer is not updated until the next chunk is processed
                        buffer = ""

                    buffer_line += buffer
                                        
                    punctuations = (',', '.', '?', '!', 'and', 'or', 'but', 'however')
                    if any(punctuation in buffer_line for punctuation in punctuations):
                        last_punctuation_index = max((buffer_line.rfind(p) + len(p) + 1) for p in punctuations if p in buffer_line)
                        extracted_text = buffer_line[:last_punctuation_index]
                        buffer_line = buffer_line[last_punctuation_index:]
                        buffers.append({'speaker': '0', 'text': extracted_text, 'translation': None})

                    # Translation loop
                    print('buffers for loop')
                    for i, buffer in enumerate(buffers):
                        print(i, buffer)
                        if buffer['translation'] is not None:
                            continue
                        if buffer['text'] == '':
                            continue

                        transcription = buffer['text']
                        buffers[i]['translation'] = translator_wrapper(transcription, mode='google')
                        buffers[i]['text'] += ('|' + buffers[i]['translation'])

                    # ----------------
                    # Process lines
                    # ----------------
                    print('Process lines')
                    lines = [{"speaker": "0", "text": ""}]
                    
                    if args.diarization:
                        await diarization.diarize(pcm_array)
                        # diarization.assign_speakers_to_chunks(chunk_history)
                        chunk_history = diarization.assign_speakers_to_chunks(chunk_history)

                    for ch in chunk_history:
                        if args.diarization and ch["speaker"] and ch["speaker"][-1] != lines[-1]["speaker"]:
                            lines.append({"speaker": ch["speaker"], "text": ch['text']})

                        else:
                            lines.append({"speaker": ch["speaker"], "text": ch['text']})

                    for i, line in enumerate(lines):
                        if line['text'].strip() == '':
                            continue
                        # translation = translator(line['text'])[0]['translation_text']
                        # translation = translation.replace(' ', '')
                        # lines[i]['text'] = line['text'] + translation
                        lines[i]['text'] = line['text']

                    # translation = translator(buffer)[0]['translation_text']
                    # translation = translation.replace(' ', '')
                    # buffer += translation

                    print('Before making response')
                    response = {"lines": buffers, "buffer": ''}
                    await websocket.send_json(response)
                    
            except Exception as e:
                print(f"Exception in ffmpeg_stdout_reader: {e}")
                break

        print("Exiting ffmpeg_stdout_reader...")

    stdout_reader_task = asyncio.create_task(ffmpeg_stdout_reader())

    try:
        while True:
            # Receive incoming WebM audio chunks from the client
            message = await websocket.receive_bytes()
            # Pass them to ffmpeg via stdin
            ffmpeg_process.stdin.write(message)
            ffmpeg_process.stdin.flush()

    except WebSocketDisconnect:
        print("WebSocket connection closed.")
    except Exception as e:
        print(f"Error in websocket loop: {e}")
    finally:
        # Clean up ffmpeg and the reader task
        try:
            ffmpeg_process.stdin.close()
        except:
            pass
        stdout_reader_task.cancel()

        try:
            ffmpeg_process.stdout.close()
        except:
            pass

        ffmpeg_process.wait()
        del online
        
        if args.diarization:
            # Stop Diart
            diarization.close()




if __name__ == "__main__":
    import uvicorn

    uvicorn.run(
        "whisper_fastapi_online_server:app", host=args.host, port=args.port, reload=True,
        log_level="info"
    )