Create router.py

router.py

@@ -0,0 +1,191 @@
+import base64
+import logging
+import math
+import tempfile
+import time
+from typing import Optional, Tuple
+
+import fastapi
+import jax.numpy as jnp
+import numpy as np
+import yt_dlp as youtube_dl
+from jax.experimental.compilation_cache import compilation_cache as cc
+from pydantic import BaseModel
+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
+
+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 = fastapi.FastAPI()
+
+class TranscriptionRequest(BaseModel):
+    audio_file: str
+    task: str = "transcribe"
+    return_timestamps: bool = False
+
+class TranscriptionResponse(BaseModel):
+    transcription: str
+    runtime: float
+
+@app.post("/transcribe", response_model=TranscriptionResponse)
+def transcribe_audio(request: TranscriptionRequest):
+    logger.info("loading audio file...")
+    if not request.audio_file:
+        logger.warning("No audio file")
+        raise fastapi.HTTPException(status_code=400, detail="No audio file submitted!")
+
+    audio_bytes = base64.b64decode(request.audio_file)
+    file_size_mb = len(audio_bytes) / (1024 * 1024)
+    if file_size_mb > FILE_LIMIT_MB:
+        logger.warning("Max file size exceeded")
+        raise fastapi.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.",
+        )
+
+    inputs = ffmpeg_read(audio_bytes, 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=request.task, return_timestamps=request.return_timestamps)
+    return TranscriptionResponse(transcription=text, runtime=runtime)
+
+@app.post("/transcribe_youtube")
+def transcribe_youtube(
+    yt_url: str, task: str = "transcribe", return_timestamps: bool = False
+) -> Tuple[str, str, float]:
+    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_str, text, runtime
+
+def _tqdm_generate(inputs: dict, task: str, return_timestamps: bool, progress: Optional[fastapi.ProgressBar] = None):
+    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 zip(dataloader, range(num_batches)):
+        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: str) -> str:
+    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: str, filename: str):
+    info_loader = youtube_dl.YoutubeDL()
+    try:
+        info = info_loader.extract_info(yt_url, download=False)
+    except youtube_dl.utils.DownloadError as err:
+        raise fastapi.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 fastapi.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 fastapi.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