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| # import whisper | |
| from faster_whisper import WhisperModel | |
| import datetime | |
| import subprocess | |
| import gradio as gr | |
| from pathlib import Path | |
| import pandas as pd | |
| import re | |
| import time | |
| import os | |
| import numpy as np | |
| from sklearn.cluster import AgglomerativeClustering | |
| from sklearn.metrics import silhouette_score | |
| from pytube import YouTube | |
| import yt_dlp | |
| import torch | |
| import pyannote.audio | |
| from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding | |
| from pyannote.audio import Audio | |
| from pyannote.core import Segment | |
| from gpuinfo import GPUInfo | |
| import wave | |
| import contextlib | |
| from transformers import pipeline | |
| import psutil | |
| whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"] | |
| source_languages = { | |
| "en": "English", | |
| "zh": "Chinese", | |
| "de": "German", | |
| "es": "Spanish", | |
| "ru": "Russian", | |
| "ko": "Korean", | |
| "fr": "French", | |
| "ja": "Japanese", | |
| "pt": "Portuguese", | |
| "tr": "Turkish", | |
| "pl": "Polish", | |
| "ca": "Catalan", | |
| "nl": "Dutch", | |
| "ar": "Arabic", | |
| "sv": "Swedish", | |
| "it": "Italian", | |
| "id": "Indonesian", | |
| "hi": "Hindi", | |
| "fi": "Finnish", | |
| "vi": "Vietnamese", | |
| "he": "Hebrew", | |
| "uk": "Ukrainian", | |
| "el": "Greek", | |
| "ms": "Malay", | |
| "cs": "Czech", | |
| "ro": "Romanian", | |
| "da": "Danish", | |
| "hu": "Hungarian", | |
| "ta": "Tamil", | |
| "no": "Norwegian", | |
| "th": "Thai", | |
| "ur": "Urdu", | |
| "hr": "Croatian", | |
| "bg": "Bulgarian", | |
| "lt": "Lithuanian", | |
| "la": "Latin", | |
| "mi": "Maori", | |
| "ml": "Malayalam", | |
| "cy": "Welsh", | |
| "sk": "Slovak", | |
| "te": "Telugu", | |
| "fa": "Persian", | |
| "lv": "Latvian", | |
| "bn": "Bengali", | |
| "sr": "Serbian", | |
| "az": "Azerbaijani", | |
| "sl": "Slovenian", | |
| "kn": "Kannada", | |
| "et": "Estonian", | |
| "mk": "Macedonian", | |
| "br": "Breton", | |
| "eu": "Basque", | |
| "is": "Icelandic", | |
| "hy": "Armenian", | |
| "ne": "Nepali", | |
| "mn": "Mongolian", | |
| "bs": "Bosnian", | |
| "kk": "Kazakh", | |
| "sq": "Albanian", | |
| "sw": "Swahili", | |
| "gl": "Galician", | |
| "mr": "Marathi", | |
| "pa": "Punjabi", | |
| "si": "Sinhala", | |
| "km": "Khmer", | |
| "sn": "Shona", | |
| "yo": "Yoruba", | |
| "so": "Somali", | |
| "af": "Afrikaans", | |
| "oc": "Occitan", | |
| "ka": "Georgian", | |
| "be": "Belarusian", | |
| "tg": "Tajik", | |
| "sd": "Sindhi", | |
| "gu": "Gujarati", | |
| "am": "Amharic", | |
| "yi": "Yiddish", | |
| "lo": "Lao", | |
| "uz": "Uzbek", | |
| "fo": "Faroese", | |
| "ht": "Haitian creole", | |
| "ps": "Pashto", | |
| "tk": "Turkmen", | |
| "nn": "Nynorsk", | |
| "mt": "Maltese", | |
| "sa": "Sanskrit", | |
| "lb": "Luxembourgish", | |
| "my": "Myanmar", | |
| "bo": "Tibetan", | |
| "tl": "Tagalog", | |
| "mg": "Malagasy", | |
| "as": "Assamese", | |
| "tt": "Tatar", | |
| "haw": "Hawaiian", | |
| "ln": "Lingala", | |
| "ha": "Hausa", | |
| "ba": "Bashkir", | |
| "jw": "Javanese", | |
| "su": "Sundanese", | |
| } | |
| source_language_list = [key[0] for key in source_languages.items()] | |
| MODEL_NAME = "vumichien/whisper-medium-jp" | |
| lang = "ja" | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| os.makedirs('output', exist_ok=True) | |
| pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") | |
| embedding_model = PretrainedSpeakerEmbedding( | |
| "speechbrain/spkrec-ecapa-voxceleb", | |
| device=torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| def transcribe(microphone, file_upload): | |
| warn_output = "" | |
| if (microphone is not None) and (file_upload is not None): | |
| warn_output = ( | |
| "WARNING: You've uploaded an audio file and used the microphone. " | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| ) | |
| elif (microphone is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| file = microphone if microphone is not None else file_upload | |
| text = pipe(file)["text"] | |
| return warn_output + text | |
| 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 yt_transcribe(yt_url): | |
| # yt = YouTube(yt_url) | |
| # html_embed_str = _return_yt_html_embed(yt_url) | |
| # stream = yt.streams.filter(only_audio=True)[0] | |
| # stream.download(filename="audio.mp3") | |
| ydl_opts = { | |
| 'format': 'bestvideo*+bestaudio/best', | |
| 'postprocessors': [{ | |
| 'key': 'FFmpegExtractAudio', | |
| 'preferredcodec': 'mp3', | |
| 'preferredquality': '192', | |
| }], | |
| 'outtmpl':'audio.%(ext)s', | |
| } | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| ydl.download([yt_url]) | |
| text = pipe("audio.mp3")["text"] | |
| return html_embed_str, text | |
| def convert_time(secs): | |
| return datetime.timedelta(seconds=round(secs)) | |
| def get_youtube(video_url): | |
| # yt = YouTube(video_url) | |
| # abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download() | |
| ydl_opts = { | |
| 'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best', | |
| } | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| info = ydl.extract_info(video_url, download=False) | |
| abs_video_path = ydl.prepare_filename(info) | |
| ydl.process_info(info) | |
| print("Success download video") | |
| print(abs_video_path) | |
| return abs_video_path | |
| def speech_to_text(video_file_path, selected_source_lang, whisper_model, num_speakers): | |
| """ | |
| # Transcribe youtube link using OpenAI Whisper | |
| 1. Using Open AI's Whisper model to seperate audio into segments and generate transcripts. | |
| 2. Generating speaker embeddings for each segments. | |
| 3. Applying agglomerative clustering on the embeddings to identify the speaker for each segment. | |
| Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper | |
| Speaker diarization model and pipeline from by https://github.com/pyannote/pyannote-audio | |
| """ | |
| # model = whisper.load_model(whisper_model) | |
| # model = WhisperModel(whisper_model, device="cuda", compute_type="int8_float16") | |
| model = WhisperModel(whisper_model, compute_type="int8") | |
| time_start = time.time() | |
| if(video_file_path == None): | |
| raise ValueError("Error no video input") | |
| print(video_file_path) | |
| try: | |
| # Read and convert youtube video | |
| _,file_ending = os.path.splitext(f'{video_file_path}') | |
| print(f'file enging is {file_ending}') | |
| audio_file = video_file_path.replace(file_ending, ".wav") | |
| print("starting conversion to wav") | |
| os.system(f'ffmpeg -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file}"') | |
| # Get duration | |
| with contextlib.closing(wave.open(audio_file,'r')) as f: | |
| frames = f.getnframes() | |
| rate = f.getframerate() | |
| duration = frames / float(rate) | |
| print(f"conversion to wav ready, duration of audio file: {duration}") | |
| # Transcribe audio | |
| options = dict(language=selected_source_lang, beam_size=5, best_of=5) | |
| transcribe_options = dict(task="transcribe", **options) | |
| segments_raw, info = model.transcribe(audio_file, **transcribe_options) | |
| # Convert back to original openai format | |
| segments = [] | |
| i = 0 | |
| for segment_chunk in segments_raw: | |
| chunk = {} | |
| chunk["start"] = segment_chunk.start | |
| chunk["end"] = segment_chunk.end | |
| chunk["text"] = segment_chunk.text | |
| segments.append(chunk) | |
| i += 1 | |
| print("transcribe audio done with fast whisper") | |
| except Exception as e: | |
| raise RuntimeError("Error converting video to audio") | |
| try: | |
| # Create embedding | |
| def segment_embedding(segment): | |
| audio = Audio() | |
| start = segment["start"] | |
| # Whisper overshoots the end timestamp in the last segment | |
| end = min(duration, segment["end"]) | |
| clip = Segment(start, end) | |
| waveform, sample_rate = audio.crop(audio_file, clip) | |
| return embedding_model(waveform[None]) | |
| embeddings = np.zeros(shape=(len(segments), 192)) | |
| for i, segment in enumerate(segments): | |
| embeddings[i] = segment_embedding(segment) | |
| embeddings = np.nan_to_num(embeddings) | |
| print(f'Embedding shape: {embeddings.shape}') | |
| if num_speakers == 0: | |
| # Find the best number of speakers | |
| score_num_speakers = {} | |
| for num_speakers in range(2, 10+1): | |
| clustering = AgglomerativeClustering(num_speakers).fit(embeddings) | |
| score = silhouette_score(embeddings, clustering.labels_, metric='euclidean') | |
| score_num_speakers[num_speakers] = score | |
| best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x]) | |
| print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score") | |
| else: | |
| best_num_speaker = num_speakers | |
| # Assign speaker label | |
| clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings) | |
| labels = clustering.labels_ | |
| for i in range(len(segments)): | |
| segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1) | |
| # Make output | |
| objects = { | |
| 'Start' : [], | |
| 'End': [], | |
| 'Speaker': [], | |
| 'Text': [] | |
| } | |
| text = '' | |
| for (i, segment) in enumerate(segments): | |
| if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]: | |
| objects['Start'].append(str(convert_time(segment["start"]))) | |
| objects['Speaker'].append(segment["speaker"]) | |
| if i != 0: | |
| objects['End'].append(str(convert_time(segments[i - 1]["end"]))) | |
| objects['Text'].append(text) | |
| text = '' | |
| text += segment["text"] + ' ' | |
| objects['End'].append(str(convert_time(segments[i - 1]["end"]))) | |
| objects['Text'].append(text) | |
| time_end = time.time() | |
| time_diff = time_end - time_start | |
| memory = psutil.virtual_memory() | |
| gpu_utilization, gpu_memory = GPUInfo.gpu_usage() | |
| gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0 | |
| gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0 | |
| system_info = f""" | |
| *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.* | |
| *Processing time: {time_diff:.5} seconds.* | |
| *GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.* | |
| """ | |
| save_path = "output/transcript_result.csv" | |
| df_results = pd.DataFrame(objects) | |
| df_results.to_csv(save_path) | |
| return df_results, system_info, save_path | |
| except Exception as e: | |
| raise RuntimeError("Error Running inference with local model", e) | |
| # ---- Gradio Layout ----- | |
| # Inspiration from https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles | |
| video_in = gr.Video(label="Video file", mirror_webcam=False) | |
| youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True) | |
| df_init = pd.DataFrame(columns=['Start', 'End', 'Speaker', 'Text']) | |
| memory = psutil.virtual_memory() | |
| selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="en", label="Spoken language in video", interactive=True) | |
| selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True) | |
| number_speakers = gr.Number(precision=0, value=0, label="Input number of speakers for better results. If value=0, model will automatic find the best number of speakers", interactive=True) | |
| system_info = gr.Markdown(f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB*") | |
| download_transcript = gr.File(label="Download transcript") | |
| transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate') | |
| title = "Whisper speaker diarization" | |
| demo = gr.Blocks(title=title) | |
| demo.encrypt = False | |
| with demo: | |
| with gr.Tab("Med Speech Pro"): | |
| gr.Markdown(''' | |
| <div> | |
| <h1 style='text-align: center'>Med Speech Pro : Lightning-Fast</h1> | |
| Description: Experience Rapid Speech Recognition and Seamless Speaker identification With SpeechPro, a cutting-edge solution for accurate Medical Transcription | |
| </div> | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| youtube_url_in.render() | |
| download_youtube_btn = gr.Button("Download Youtube video") | |
| download_youtube_btn.click(get_youtube, [youtube_url_in], [ | |
| video_in]) | |
| print(video_in) | |
| with gr.Row(): | |
| with gr.Column(): | |
| video_in.render() | |
| with gr.Column(): | |
| gr.Markdown('''. | |
| ''') | |
| selected_source_lang.render() | |
| selected_whisper_model.render() | |
| number_speakers.render() | |
| transcribe_btn = gr.Button("Transcribe Now") | |
| transcribe_btn.click(speech_to_text, | |
| [video_in, selected_source_lang, selected_whisper_model, number_speakers], | |
| [transcription_df, system_info, download_transcript] | |
| ) | |
| with gr.Row(): | |
| gr.Markdown(''' | |
| ##### Results | |
| ##### ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| download_transcript.render() | |
| transcription_df.render() | |
| system_info.render() | |
| gr.Markdown('''<center><img src='https://visitor-badge.glitch.me/badge?page_id=WhisperDiarizationSpeakers' alt='visitor badge'><a href="https://opensource.org/licenses/Apache-2.0"><img src='https://img.shields.io/badge/License-Apache_2.0-blue.svg' alt='License: Apache 2.0'></center>''') | |
| demo.launch(debug=True) |