import logging import math import os import tempfile import time from multiprocessing import Pool import gradio as gr 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 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 = 1000 YT_LENGTH_LIMIT_S = 7200 # limit to 2 hour YouTube files title = "Whisper JAX: The Fastest Whisper API ⚡️" description = """Whisper JAX is an optimised implementation of the [Whisper model](https://huggingface.co/openai/whisper-large-v3) by OpenAI. It runs on JAX with a TPU v4-8 in the backend. Compared to PyTorch on an A100 GPU, it is over [**70x faster**](https://github.com/sanchit-gandhi/whisper-jax#benchmarks), making it the fastest Whisper API available. Note that at peak times, you may find yourself in the queue for this demo. When you submit a request, your queue position will be shown in the top right-hand side of the demo pane. Once you reach the front of the queue, your audio file will be transcribed, with the progress displayed through a progress bar. To skip the queue, you may wish to create your own inference endpoint, details for which can be found in the [Whisper JAX repository](https://github.com/sanchit-gandhi/whisper-jax#creating-an-endpoint). """ article = "Whisper large-v3 model by OpenAI. Backend running JAX on a TPU v4-8 through the generous support of the [TRC](https://sites.research.google/trc/about/) programme. Whisper JAX [code](https://github.com/sanchit-gandhi/whisper-jax) and Gradio demo by 🤗 Hugging Face." language_names = sorted(TO_LANGUAGE_CODE.keys()) 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) def identity(batch): return batch # Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50 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 if __name__ == "__main__": 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 pool = Pool(NUM_PROC) # 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") def tqdm_generate(inputs: dict, task: str, return_timestamps: bool, progress: gr.Progress): 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) dummy_batches = list( range(num_batches) ) # Gradio progress bar not compatible with generator, see https://github.com/gradio-app/gradio/issues/3841 dataloader = pipeline.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE) progress(0, desc="Pre-processing audio file...") logger.info("pre-processing audio file...") dataloader = pool.map(identity, dataloader) logger.info("done post-processing") 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, progress.tqdm(dummy_batches, desc="Transcribing...")): 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 transcribe_chunked_audio(inputs, task, return_timestamps, progress=gr.Progress()): progress(0, desc="Loading audio file...") logger.info("loading audio file...") if inputs is None: logger.warning("No audio file") raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.") file_size_mb = os.stat(inputs).st_size / (1024 * 1024) if file_size_mb > FILE_LIMIT_MB: logger.warning("Max file size exceeded") raise gr.Error( 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(inputs, "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, progress=progress) 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 gr.Error(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 gr.Error(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 gr.Error(str(err)) def transcribe_youtube(yt_url, task, return_timestamps, progress=gr.Progress()): progress(0, desc="Loading audio file...") 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, progress=progress) return html_embed_str, text, runtime microphone_chunked = gr.Interface( fn=transcribe_chunked_audio, inputs=[ gr.Audio(source="microphone", type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), gr.Checkbox(value=False, label="Return timestamps"), ], outputs=[ gr.Textbox(label="Transcription", show_copy_button=True), gr.Textbox(label="Transcription Time (s)"), ], allow_flagging="never", title=title, description=description, article=article, ) audio_chunked = gr.Interface( fn=transcribe_chunked_audio, inputs=[ gr.Audio(source="upload", label="Audio file", type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), gr.Checkbox(value=False, label="Return timestamps"), ], outputs=[ gr.Textbox(label="Transcription", show_copy_button=True), gr.Textbox(label="Transcription Time (s)"), ], allow_flagging="never", title=title, description=description, article=article, ) youtube = gr.Interface( fn=transcribe_youtube, inputs=[ gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), gr.Checkbox(value=False, label="Return timestamps"), ], outputs=[ gr.HTML(label="Video"), gr.Textbox(label="Transcription", show_copy_button=True), gr.Textbox(label="Transcription Time (s)"), ], allow_flagging="never", title=title, examples=[["https://www.youtube.com/watch?v=m8u-18Q0s7I", "transcribe", False]], cache_examples=False, description=description, article=article, ) demo = gr.Blocks() with demo: gr.TabbedInterface([microphone_chunked, audio_chunked, youtube], ["Microphone", "Audio File", "YouTube"]) demo.queue(max_size=5) demo.launch(show_api=False)