app.py

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

app = FastAPI(title="Whisper JAX: The Fastest Whisper API ⚡️")

logger = logging.getLogger("whisper-jax-app")
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s;%(levelname)s;%(message)s", "%Y-%m-%d %H:%M:%S")
ch.setFormatter(formatter)
logger.addHandler(ch)

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

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.debug("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.debug(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.debug("Starting transcribe_chunked_audio function")
    logger.debug(f"Received parameters - task: {task}, return_timestamps: {return_timestamps}")
    
    logger.debug("Checking for audio file...")
    if not audio_file:
        logger.warning("No audio file")
        raise HTTPException(status_code=400, detail="No audio file submitted!")
    
    logger.debug(f"Audio file received: {audio_file.filename}")
    
    try:
        # Read the file content
        file_content = await audio_file.read()
        file_size = len(file_content)
        file_size_mb = file_size / (1024 * 1024)
        logger.debug(f"File size: {file_size} bytes ({file_size_mb:.2f}MB)")
    except Exception as e:
        logger.error(f"Error reading file: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error reading file: {str(e)}")

    if file_size_mb > FILE_LIMIT_MB:
        logger.warning(f"Max file size exceeded: {file_size_mb:.2f}MB > {FILE_LIMIT_MB}MB")
        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.")

    try:
        logger.debug("Performing ffmpeg read on audio file")
        inputs = ffmpeg_read(file_content, pipeline.feature_extractor.sampling_rate)
        inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate}
        logger.debug("ffmpeg read completed successfully")
    except Exception as e:
        logger.error(f"Error in ffmpeg read: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error processing audio file: {str(e)}")

    logger.debug("Calling tqdm_generate to transcribe audio")
    try:
        text, runtime = tqdm_generate(inputs, task=task, return_timestamps=return_timestamps)
        logger.debug(f"Transcription completed. Runtime: {runtime:.2f}s")
    except Exception as e:
        logger.error(f"Error in tqdm_generate: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Error transcribing audio: {str(e)}")

    logger.debug("Transcribe_chunked_audio function completed successfully")
    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.debug("Loading YouTube file...")
    try:
        html_embed_str = _return_yt_html_embed(yt_url)
    except Exception as e:
        logger.error("Error generating YouTube HTML embed:", exc_info=True)
        raise HTTPException(status_code=500, detail="Error generating YouTube HTML embed")

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        try:
            logger.debug("Downloading YouTube audio...")
            download_yt_audio(yt_url, filepath)
        except Exception as e:
            logger.error("Error downloading YouTube audio:", exc_info=True)
            raise HTTPException(status_code=500, detail="Error downloading YouTube audio")

        try:
            logger.debug(f"Opening downloaded audio file: {filepath}")
            with open(filepath, "rb") as f:
                inputs = f.read()
        except Exception as e:
            logger.error("Error reading downloaded audio file:", exc_info=True)
            raise HTTPException(status_code=500, detail="Error reading downloaded audio file")

    inputs = ffmpeg_read(inputs, pipeline.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate}
    logger.debug("Done loading YouTube file")

    try:
        logger.debug("Calling tqdm_generate to transcribe YouTube audio")
        text, runtime = tqdm_generate(inputs, task=task, return_timestamps=return_timestamps)
    except Exception as e:
        logger.error("Error transcribing YouTube audio:", exc_info=True)
        raise HTTPException(status_code=500, detail="Error transcribing YouTube audio")

    return {"html_embed": html_embed_str, "text": text, "runtime": runtime}

def tqdm_generate(inputs: dict, task: str, return_timestamps: bool):
    logger.debug(f"Starting tqdm_generate - task: {task}, return_timestamps: {return_timestamps}")
    
    inputs_len = inputs["array"].shape[0]
    logger.debug(f"Input array length: {inputs_len}")
    
    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)
    logger.debug(f"Number of samples: {num_samples}, Number of batches: {num_batches}")

    logger.debug("Preprocessing audio for inference")
    try:
        dataloader = pipeline.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE)
        logger.debug("Preprocessing completed successfully")
    except Exception as e:
        logger.error(f"Error in preprocessing: {str(e)}", exc_info=True)
        raise

    model_outputs = []
    start_time = time.time()
    logger.debug("Starting transcription...")
    
    try:
        for i, batch in enumerate(dataloader):
            logger.debug(f"Processing batch {i+1}/{num_batches} with {len(batch)} samples")
            batch_output = pipeline.forward(batch, batch_size=BATCH_SIZE, task=task, return_timestamps=True)
            model_outputs.append(batch_output)
            logger.debug(f"Batch {i+1} processed successfully")
    except Exception as e:
        logger.error(f"Error during batch processing: {str(e)}", exc_info=True)
        raise

    runtime = time.time() - start_time
    logger.debug(f"Transcription completed in {runtime:.2f}s")

    logger.debug("Post-processing transcription results")
    try:
        post_processed = pipeline.postprocess(model_outputs, return_timestamps=True)
        logger.debug("Post-processing completed successfully")
    except Exception as e:
        logger.error(f"Error in post-processing: {str(e)}", exc_info=True)
        raise

    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.debug("tqdm_generate function completed successfully")
    return text, runtime

def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str

def download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()
    try:
        logger.debug(f"Extracting info for YouTube URL: {yt_url}")
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        logger.error("Error extracting YouTube info:", exc_info=True)
        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:
            logger.debug(f"Downloading YouTube audio to {filename}")
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            logger.error("Error downloading YouTube audio:", exc_info=True)
            raise HTTPException(status_code=400, detail=str(err))

def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
    if seconds is not None:
        milliseconds = round(seconds * 1000.0)

        hours = milliseconds // 3_600_000
        milliseconds -= hours * 3_600_000

        minutes = milliseconds // 60_000
        milliseconds -= minutes * 60_000

        seconds = milliseconds // 1_000
        milliseconds -= seconds * 1_000

        hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
        return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
    else:
        # we have a malformed timestamp so just return it as is
        return seconds