import base64 import os from functools import partial from multiprocessing import Pool import gradio as gr import numpy as np import requests from processing_whisper import WhisperPrePostProcessor from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE from transformers.pipelines.audio_utils import ffmpeg_read title = "Whisper JAX: The Fastest Whisper API ⚡️" description = """Whisper JAX is an optimised implementation of the [Whisper model](https://huggingface.co/openai/whisper-large-v2) 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 using microphone or audio file requires the audio input to be transferred from the Gradio demo to the TPU, which for large audio files can be slow. We recommend using YouTube where possible, since this directly downloads the audio file to the TPU, skipping the file transfer step. """ API_URL = os.getenv("API_URL") API_URL_FROM_FEATURES = os.getenv("API_URL_FROM_FEATURES") article = "Whisper large-v2 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()) CHUNK_LENGTH_S = 30 BATCH_SIZE = 16 NUM_PROC = 16 FILE_LIMIT_MB = 1000 def query(payload): response = requests.post(API_URL, json=payload) return response.json(), response.status_code def inference(inputs, language=None, task=None, return_timestamps=False): payload = {"inputs": inputs, "task": task, "return_timestamps": return_timestamps} # langauge can come as an empty string from the Gradio `None` default, so we handle it separately if language: payload["language"] = language data, status_code = query(payload) if status_code == 200: text = data["text"] else: text = data["detail"] if return_timestamps: timestamps = data["chunks"] else: timestamps = None return text, timestamps def chunked_query(payload): response = requests.post(API_URL_FROM_FEATURES, json=payload) return response.json() def forward(batch, task=None, return_timestamps=False): feature_shape = batch["input_features"].shape batch["input_features"] = base64.b64encode(batch["input_features"].tobytes()).decode() outputs = chunked_query( {"batch": batch, "task": task, "return_timestamps": return_timestamps, "feature_shape": feature_shape} ) outputs["tokens"] = np.asarray(outputs["tokens"]) return outputs if __name__ == "__main__": processor = WhisperPrePostProcessor.from_pretrained("openai/whisper-large-v2") pool = Pool(NUM_PROC) def transcribe_chunked_audio(inputs, task, return_timestamps): file_size_mb = os.stat(inputs).st_size / (1024 * 1024) if file_size_mb > FILE_LIMIT_MB: return f"ERROR: File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB.", None with open(inputs, "rb") as f: inputs = f.read() inputs = ffmpeg_read(inputs, processor.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": processor.feature_extractor.sampling_rate} dataloader = processor.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE) try: model_outputs = pool.map(partial(forward, task=task, return_timestamps=return_timestamps), dataloader) except ValueError as err: # pre-processor does all the necessary compatibility checks for our audio inputs return err, None post_processed = processor.postprocess(model_outputs, return_timestamps=return_timestamps) timestamps = post_processed.get("chunks") return post_processed["text"], timestamps def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def transcribe_youtube(yt_url, task, return_timestamps): html_embed_str = _return_yt_html_embed(yt_url) text, timestamps = inference(inputs=yt_url, task=task, return_timestamps=return_timestamps) return html_embed_str, text, timestamps microphone_chunked = gr.Interface( fn=transcribe_chunked_audio, inputs=[ gr.inputs.Audio(source="microphone", optional=True, type="filepath"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(default=False, label="Return timestamps"), ], outputs=[ gr.outputs.Textbox(label="Transcription"), gr.outputs.Textbox(label="Timestamps"), ], allow_flagging="never", title=title, description=description, article=article, ) audio_chunked = gr.Interface( fn=transcribe_chunked_audio, inputs=[ gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(default=False, label="Return timestamps"), ], outputs=[ gr.outputs.Textbox(label="Transcription"), gr.outputs.Textbox(label="Timestamps"), ], allow_flagging="never", title=title, description=description, article=article, ) youtube = gr.Interface( fn=transcribe_youtube, inputs=[ gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(default=False, label="Return timestamps"), ], outputs=[ gr.outputs.HTML(label="Video"), gr.outputs.Textbox(label="Transcription"), gr.outputs.Textbox(label="Timestamps"), ], 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=3) demo.launch(show_api=False)