app.py

import os
import tempfile
import time

import gradio as gr
import numpy as np
import torch
import yt_dlp as youtube_dl
from gradio_client import Client
from pyannote.audio import Pipeline
from transformers.pipelines.audio_utils import ffmpeg_read


YT_LENGTH_LIMIT_S = 36000  # limit to 1 hour YouTube files
SAMPLING_RATE = 16000

API_URL = "https://sanchit-gandhi-whisper-jax.hf.space/"

# set up the Gradio client
client = Client(API_URL)

# set up the diarization pipeline
diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=True)


def format_string(timestamp):
    """
    Reformat a timestamp string from (HH:)MM:SS to float seconds. Note that the hour column
    is optional, and is appended within the function if not input.

    Args:
        timestamp (str):
            Timestamp in string format, either MM:SS or HH:MM:SS.
    Returns:
        seconds (float):
            Total seconds corresponding to the input timestamp.
    """
    split_time = timestamp.split(":")
    split_time = [float(sub_time) for sub_time in split_time]

    if len(split_time) == 2:
        split_time.insert(0, 0)

    seconds = split_time[0] * 3600 + split_time[1] * 60 + split_time[2]
    return seconds


# Adapted 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 = "."):
    """
    Reformat a timestamp from a float of seconds to a string in format (HH:)MM:SS. Note that the hour
    column is optional, and is appended in the function if the number of hours > 0.

    Args:
        seconds (float):
            Total seconds corresponding to the input timestamp.
    Returns:
        timestamp (str):
            Timestamp in string format, either MM:SS or HH:MM:SS.
    """
    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


def format_as_transcription(raw_segments):
    return "\n".join(
        [
            f"{chunk['speaker']} [{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
            for chunk in raw_segments
        ]
    )


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 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_length_s = format_string(file_length)

    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 align(transcription, segments, group_by_speaker=True):
    transcription_split = transcription.split("\n")

    # re-format transcription from string to List[Dict]
    transcript = []
    for chunk in transcription_split:
        start_end, transcription = chunk[1:].split("] ")
        start, end = start_end.split("->")

        transcript.append({"timestamp": (format_string(start), format_string(end)), "text": transcription})

    # diarizer output may contain consecutive segments from the same speaker (e.g. {(0 -> 1, speaker_1), (1 -> 1.5, speaker_1), ...})
    # we combine these segments to give overall timestamps for each speaker's turn (e.g. {(0 -> 1.5, speaker_1), ...})
    new_segments = []
    prev_segment = cur_segment = segments[0]

    for i in range(1, len(segments)):
        cur_segment = segments[i]

        # check if we have changed speaker ("label")
        if cur_segment["label"] != prev_segment["label"] and i < len(segments):
            # add the start/end times for the super-segment to the new list
            new_segments.append(
                {
                    "segment": {"start": prev_segment["segment"]["start"], "end": cur_segment["segment"]["start"]},
                    "speaker": prev_segment["label"],
                }
            )
            prev_segment = segments[i]

    # add the last segment(s) if there was no speaker change
    new_segments.append(
        {
            "segment": {"start": prev_segment["segment"]["start"], "end": cur_segment["segment"]["end"]},
            "speaker": prev_segment["label"],
        }
    )

    # get the end timestamps for each chunk from the ASR output
    end_timestamps = np.array([chunk["timestamp"][-1] for chunk in transcript])
    segmented_preds = []

    # align the diarizer timestamps and the ASR timestamps
    for segment in new_segments:
        # get the diarizer end timestamp
        end_time = segment["segment"]["end"]
        # find the ASR end timestamp that is closest to the diarizer's end timestamp and cut the transcript to here
        upto_idx = np.argmin(np.abs(end_timestamps - end_time))

        if group_by_speaker:
            segmented_preds.append(
                {
                    "speaker": segment["speaker"],
                    "text": "".join([chunk["text"] for chunk in transcript[: upto_idx + 1]]),
                    "timestamp": (transcript[0]["timestamp"][0], transcript[upto_idx]["timestamp"][1]),
                }
            )
        else:
            for i in range(upto_idx + 1):
                segmented_preds.append({"speaker": segment["speaker"], **transcript[i]})

        # crop the transcripts and timestamp lists according to the latest timestamp (for faster argmin)
        transcript = transcript[upto_idx + 1 :]
        end_timestamps = end_timestamps[upto_idx + 1 :]

    # final post-processing
    transcription = format_as_transcription(segmented_preds)
    return transcription


def transcribe(audio_path, group_by_speaker=True):
    # run Whisper JAX asynchronously using Gradio client (endpoint)
    job = client.submit(
        audio_path,
        "transcribe",
        True,
        api_name="/predict_1",
    )

    # run diarization while we wait for Whisper JAX
    diarization = diarization_pipeline(audio_path)
    segments = diarization.for_json()["content"]

    # only fetch the transcription result after performing diarization
    transcription, _ = job.result()

    # align the ASR transcriptions and diarization timestamps
    transcription = align(transcription, segments, group_by_speaker=group_by_speaker)

    return transcription


def transcribe_yt(yt_url, group_by_speaker=True):
    # run Whisper JAX asynchronously using Gradio client (endpoint)
    job = client.submit(
        yt_url,
        "transcribe",
        True,
        api_name="/predict_2",
    )

    _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, SAMPLING_RATE)
    inputs = torch.from_numpy(inputs).float()
    inputs = inputs.unsqueeze(0)

    diarization = diarization_pipeline(
        {"waveform": inputs, "sample_rate": SAMPLING_RATE},
    )
    segments = diarization.for_json()["content"]

    # only fetch the transcription result after performing diarization
    transcription, _ = job.result()

    # align the ASR transcriptions and diarization timestamps
    transcription = align(transcription, segments, group_by_speaker=group_by_speaker)

    return transcription


title = "Whisper JAX + Speaker Diarization ⚡️"

description = """Combine the speed of Whisper JAX with pyannote speaker diarization to transcribe meetings in super fast time.
"""

article = "Whisper large-v2 model by OpenAI. Speaker diarization model by pyannote. Whisper JAX 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."

microphone = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", optional=True, type="filepath"),
        gr.inputs.Checkbox(default=True, label="Group by speaker"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
    ],
    allow_flagging="never",
    title=title,
    description=description,
    article=article,
)

audio_file = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
        gr.inputs.Checkbox(default=True, label="Group by speaker"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
    ],
    allow_flagging="never",
    title=title,
    description=description,
    article=article,
)

youtube = gr.Interface(
    fn=transcribe_yt,
    inputs=[
        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.inputs.Checkbox(default=True, label="Group by speaker"),
    ],
    outputs=[
        gr.outputs.HTML(label="Video"),
        gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
    ],
    allow_flagging="never",
    title=title,
    examples=[["https://www.youtube.com/watch?v=m8u-18Q0s7I", True]],
    cache_examples=False,
    description=description,
    article=article,
)

demo = gr.Blocks()

with demo:
    gr.TabbedInterface([microphone, audio_file, youtube], ["Microphone", "Audio File", "YouTube"])

demo.queue(concurrency_count=1, max_size=5)
demo.launch()