Spaces:
Sleeping
Sleeping
| 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'<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.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) | |