import logging import math import os import tempfile import time import yt_dlp as youtube_dl from fastapi import FastAPI, UploadFile, Form, HTTPException from fastapi.responses import HTMLResponse import jax.numpy as jnp import numpy as np from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE from transformers.pipelines.audio_utils import ffmpeg_read from whisper_jax import FlaxWhisperPipline cc.initialize_cache("./jax_cache") checkpoint = "openai/whisper-large-v3" BATCH_SIZE = 32 CHUNK_LENGTH_S = 30 NUM_PROC = 32 FILE_LIMIT_MB = 10000 YT_LENGTH_LIMIT_S = 15000 # limit to 2 hour YouTube files app = FastAPI(title="Whisper JAX: The Fastest Whisper API ⚡️") logger = logging.getLogger("whisper-jax-app") logger.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.INFO) formatter = logging.Formatter("%(asctime)s;%(levelname)s;%(message)s", "%Y-%m-%d %H:%M:%S") ch.setFormatter(formatter) logger.addHandler(ch) pipeline = FlaxWhisperPipline(checkpoint, dtype=jnp.bfloat16, batch_size=BATCH_SIZE) stride_length_s = CHUNK_LENGTH_S / 6 chunk_len = round(CHUNK_LENGTH_S * pipeline.feature_extractor.sampling_rate) stride_left = stride_right = round(stride_length_s * pipeline.feature_extractor.sampling_rate) step = chunk_len - stride_left - stride_right # do a pre-compile step so that the first user to use the demo isn't hit with a long transcription time logger.info("compiling forward call...") start = time.time() random_inputs = { "input_features": np.ones( (BATCH_SIZE, pipeline.model.config.num_mel_bins, 2 * pipeline.model.config.max_source_positions) ) } random_timestamps = pipeline.forward(random_inputs, batch_size=BATCH_SIZE, return_timestamps=True) compile_time = time.time() - start logger.info(f"compiled in {compile_time}s") @app.post("/transcribe_audio") async def transcribe_chunked_audio(audio_file: UploadFile, task: str = "transcribe", return_timestamps: bool = False): logger.info("loading audio file...") if not audio_file: logger.warning("No audio file") raise HTTPException(status_code=400, detail="No audio file submitted!") file_size_mb = os.stat(audio_file.filename).st_size / (1024 * 1024) if file_size_mb > FILE_LIMIT_MB: logger.warning("Max file size exceeded") raise HTTPException(status_code=400, detail=f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB.") with open(audio_file.filename, "rb") as f: inputs = f.read() inputs = ffmpeg_read(inputs, pipeline.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate} logger.info("done loading") text, runtime = tqdm_generate(inputs, task=task, return_timestamps=return_timestamps) return {"text": text, "runtime": runtime} @app.post("/transcribe_youtube") async def transcribe_youtube(yt_url: str = Form(...), task: str = "transcribe", return_timestamps: bool = False): logger.info("loading youtube file...") html_embed_str = _return_yt_html_embed(yt_url) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "video.mp4") download_yt_audio(yt_url, filepath) with open(filepath, "rb") as f: inputs = f.read() inputs = ffmpeg_read(inputs, pipeline.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate} logger.info("done loading...") text, runtime = tqdm_generate(inputs, task=task, return_timestamps=return_timestamps) return {"html_embed": html_embed_str, "text": text, "runtime": runtime} def tqdm_generate(inputs: dict, task: str, return_timestamps: bool): inputs_len = inputs["array"].shape[0] all_chunk_start_idx = np.arange(0, inputs_len, step) num_samples = len(all_chunk_start_idx) num_batches = math.ceil(num_samples / BATCH_SIZE) dataloader = pipeline.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE) model_outputs = [] start_time = time.time() logger.info("transcribing...") # iterate over our chunked audio samples - always predict timestamps to reduce hallucinations for batch in dataloader: model_outputs.append(pipeline.forward(batch, batch_size=BATCH_SIZE, task=task, return_timestamps=True)) runtime = time.time() - start_time logger.info("done transcription") logger.info("post-processing...") post_processed = pipeline.postprocess(model_outputs, return_timestamps=True) text = post_processed["text"] if return_timestamps: timestamps = post_processed.get("chunks") timestamps = [ f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps ] text = "\n".join(str(feature) for feature in timestamps) logger.info("done post-processing") return text, runtime def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def download_yt_audio(yt_url, filename): info_loader = youtube_dl.YoutubeDL() try: info = info_loader.extract_info(yt_url, download=False) except youtube_dl.utils.DownloadError as err: raise HTTPException(status_code=400, detail=str(err)) file_length = info["duration_string"] file_h_m_s = file_length.split(":") file_h_m_s = [int(sub_length) for sub_length in file_h_m_s] if len(file_h_m_s) == 1: file_h_m_s.insert(0, 0) if len(file_h_m_s) == 2: file_h_m_s.insert(0, 0) file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2] if file_length_s > YT_LENGTH_LIMIT_S: yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)) file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s)) raise HTTPException(status_code=400, detail=f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"} with youtube_dl.YoutubeDL(ydl_opts) as ydl: try: ydl.download([yt_url]) except youtube_dl.utils.ExtractorError as err: raise HTTPException(status_code=400, detail=str(err))