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 = {} # 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)}]:" 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'
' 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.inputs.Audio(source="microphone", type="filepath", optional=True), gr.inputs.Textbox(lines=1, placeholder="Prompt", optional=True), ], 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.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"), gr.inputs.Textbox(lines=1, placeholder="Prompt", optional=True), ], 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.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.inputs.Textbox(lines=1, placeholder="Prompt", optional=True), ], 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.launch(enable_queue=True)