app.py

import base64
import math
import os
import time
from multiprocessing import Pool

import gradio as gr
import numpy as np
import pytube
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, task=None, return_timestamps=False):
    payload = {"inputs": inputs, "task": task, "return_timestamps": return_timestamps}

    data, status_code = query(payload)

    if status_code == 200:
        text = data["text"]
    else:
        text = data["detail"]

    timestamps = data.get("chunks")
    if timestamps is not None:
        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)
    return text


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


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__":
    processor = WhisperPrePostProcessor.from_pretrained("openai/whisper-large-v2")
    stride_length_s = CHUNK_LENGTH_S / 6
    chunk_len = round(CHUNK_LENGTH_S * processor.feature_extractor.sampling_rate)
    stride_left = stride_right = round(stride_length_s * processor.feature_extractor.sampling_rate)
    step = chunk_len - stride_left - stride_right
    pool = Pool(NUM_PROC)

    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 = processor.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE)
        progress(0, desc="Pre-processing audio file...")
        dataloader = pool.map(identity, dataloader)

        model_outputs = []
        start_time = time.time()
        # iterate over our chunked audio samples
        for batch, _ in zip(dataloader, progress.tqdm(dummy_batches, desc="Transcribing...")):
            model_outputs.append(forward(batch, task=task, return_timestamps=return_timestamps))
        runtime = time.time() - start_time

        post_processed = processor.postprocess(model_outputs, return_timestamps=return_timestamps)
        text = post_processed["text"]
        timestamps = post_processed.get("chunks")
        if timestamps is not None:
            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)
        return text, runtime

    def transcribe_chunked_audio(inputs, task, return_timestamps, progress=gr.Progress()):
        progress(0, desc="Loading audio file...")
        file_size_mb = os.stat(inputs).st_size / (1024 * 1024)
        if file_size_mb > FILE_LIMIT_MB:
            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, processor.feature_extractor.sampling_rate)
        inputs = {"array": inputs, "sampling_rate": processor.feature_extractor.sampling_rate}
        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'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
            " </center>"
        )
        return HTML_str

    def transcribe_youtube(yt_url, task, return_timestamps, progress=gr.Progress(), max_filesize=75.0):
        progress(0, desc="Loading audio file...")
        html_embed_str = _return_yt_html_embed(yt_url)
        try:
            yt = pytube.YouTube(yt_url)
            stream = yt.streams.filter(only_audio=True)[0]
        except KeyError:
            raise gr.Error("An error occurred while loading the YouTube video. Please try again.")

        if stream.filesize_mb > max_filesize:
            raise gr.Error(f"Maximum YouTube file size is {max_filesize}MB, got {stream.filesize_mb:.2f}MB.")

        stream.download(filename="audio.mp3")

        with open("audio.mp3", "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}
        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.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").style(show_copy_button=True),
            gr.outputs.Textbox(label="Transcription Time (s)"),
        ],
        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").style(show_copy_button=True),
            gr.outputs.Textbox(label="Transcription Time (s)"),
        ],
        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").style(show_copy_button=True),
            gr.outputs.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(concurrency_count=3, max_size=5)
    demo.launch(show_api=False, max_threads=10)