app.py

import spaces
import os
import time
import tempfile
from math import floor
from typing import Optional, List, Dict, Any

import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read


# configuration
MODEL_NAME = "kotoba-tech/kotoba-whisper-v1.1"
BATCH_SIZE = 16
CHUNK_LENGTH_S = 15
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
# device setting
# if torch.cuda.is_available():
#     torch_dtype = torch.bfloat16
#     device = "cuda:0"
#     model_kwargs = {'attn_implementation': 'sdpa'}
# else:
#     torch_dtype = torch.float32
#     device = "cpu"
#     model_kwargs = {}
device = "cuda"
torch_dtype = torch.bfloat16
model_kwargs = {'attn_implementation': 'sdpa'}

# define the pipeline
pipe = pipeline(
    model=MODEL_NAME,
    chunk_length_s=CHUNK_LENGTH_S,
    batch_size=BATCH_SIZE,
    torch_dtype=torch_dtype,
    device=device,
    model_kwargs=model_kwargs,
    trust_remote_code=True
)


def format_time(start: Optional[float], end: Optional[float]):

    def _format_time(seconds: Optional[float]):
        if seconds is None:
            return "complete    "
        minutes = floor(seconds / 60)
        hours = floor(seconds / 3600)
        seconds = seconds - hours * 3600 - minutes * 60
        m_seconds = floor(round(seconds - floor(seconds), 3) * 10 ** 3)
        seconds = floor(seconds)
        return f'{hours:02}:{minutes:02}:{seconds:02}.{m_seconds:03}'

    return f"[{_format_time(start)}-> {_format_time(end)}]:"


@spaces.GPU
def get_prediction(inputs, prompt: Optional[str]):
    generate_kwargs = {"language": "japanese", "task": "transcribe"}
    if prompt:
        generate_kwargs['prompt_ids'] = pipe.tokenizer.get_prompt_ids(prompt, return_tensors='pt').to(device)
    prediction = pipe(inputs, return_timestamps=True, generate_kwargs=generate_kwargs)
    text = "".join([c['text'] for c in prediction['chunks']])
    text_timestamped = "\n".join([
        f"{format_time(*c['timestamp'])} {c['text']}" for c in prediction['chunks']
    ])
    return text, text_timestamped


def transcribe(inputs: str, prompt):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    with open(inputs, "rb") as f:
        inputs = f.read()
    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
    return get_prediction(inputs, prompt)


def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    return f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe> </center>'


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 yt_transcribe(yt_url, prompt):
    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, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
    text, text_timestamped = get_prediction(inputs, prompt)
    return html_embed_str, text, text_timestamped


demo = gr.Blocks()
mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="microphone", type="filepath"),
        gr.Textbox(lines=1, placeholder="Prompt"),
    ],
    outputs=["text", "text"],
    # layout="horizontal",
    # theme="huggingface",
    title=f"Transcribe Audio with {os.path.basename(MODEL_NAME)}",
    description=f"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the Kotoba-Whisper checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of arbitrary length.",
    allow_flagging="never",
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="upload", type="filepath", label="Audio file"),
        gr.Textbox(lines=1, placeholder="Prompt"),
    ],
    outputs=["text", "text"],
    # layout="horizontal",
    # theme="huggingface",
    title=f"Transcribe Audio with {os.path.basename(MODEL_NAME)}",
    description=f"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses Kotoba-Whisper checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of arbitrary length.",
    allow_flagging="never",
)
yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.Textbox(lines=1, placeholder="Prompt"),
    ],
    outputs=["html", "text", "text"],
    # layout="horizontal",
    # theme="huggingface",
    title=f"Transcribe YouTube with {os.path.basename(MODEL_NAME)}",
    description=f"Transcribe long-form YouTube videos with the click of a button! Demo uses Kotoba-Whisper checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of arbitrary length.",
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])

demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False, show_error=True)