################################################################################################# # Taking code from https://huggingface.co/spaces/vumichien/Whisper_speaker_diarization/blob/main/app.py 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 embedding_model = PretrainedSpeakerEmbedding( "speechbrain/spkrec-ecapa-voxceleb", device=torch.device("cuda" if torch.cuda.is_available() else "cpu")) def fast_transcription(audio_file, whisper_model, language): """ # 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: # 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=language, 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") #text from the list return [str(s["start"]) + " " + s["text"] for s in segments] #pd.DataFrame(segments) import datetime def convert_time(secs): return datetime.timedelta(seconds=round(secs)) def speech_to_text(audio_file, 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 = "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)