Update app.py

app.py

@@ -17,40 +17,41 @@ from transformers.pipelines.audio_utils import ffmpeg_read
 
 from whisper_jax import FlaxWhisperPipline
 
-cc.initialize_cache("./jax_cache")
-checkpoint = "openai/whisper-large-v3"
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("whisper-jax-app")
 
-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
+try:
+    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
+
+    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
+    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")
 
-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")
+except Exception as e:
+    logger.error(f"Error during initialization: {str(e)}")
+    raise
 
 app = fastapi.FastAPI()
 
@@ -65,128 +66,150 @@ class TranscriptionResponse(BaseModel):
 
 @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)
+    try:
+        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)
+    except Exception as e:
+        logger.error(f"Error in transcribe_audio: {str(e)}")
+        raise fastapi.HTTPException(status_code=500, detail=f"An error occurred during transcription: {str(e)}")
 
 @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
+    try:
+        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
+    except Exception as e:
+        logger.error(f"Error in transcribe_youtube: {str(e)}")
+        raise fastapi.HTTPException(status_code=500, detail=f"An error occurred during YouTube transcription: {str(e)}")
 
 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 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
+    try:
+        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...")
+        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
+    except Exception as e:
+        logger.error(f"Error in _tqdm_generate: {str(e)}")
+        raise
 
 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.",
+        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
+    except Exception as e:
+        logger.error(f"Error in _return_yt_html_embed: {str(e)}")
+        raise
 
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+def _download_yt_audio(yt_url: str, filename: str):
+    try:
+        info_loader = youtube_dl.YoutubeDL()
         try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
+            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))
+    except Exception as e:
+        logger.error(f"Error in _download_yt_audio: {str(e)}")
+        raise
+
 def _format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
-    if seconds is not None:
-        milliseconds = round(seconds * 1000.0)
+    try:
+        if seconds is not None:
+            milliseconds = round(seconds * 1000.0)
 
-        hours = milliseconds // 3_600_000
-        milliseconds -= hours * 3_600_000
+            hours = milliseconds // 3_600_000
+            milliseconds -= hours * 3_600_000
 
-        minutes = milliseconds // 60_000
-        milliseconds -= minutes * 60_000
+            minutes = milliseconds // 60_000
+            milliseconds -= minutes * 60_000
 
-        seconds = milliseconds // 1_000
-        milliseconds -= seconds * 1_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
+            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:
+            return seconds
+    except Exception as e:
+        logger.error(f"Error in _format_timestamp: {str(e)}")
+        raise