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import gradio as gr
import whisper
import datetime
import torch
import subprocess
import os
from pyannote.audio import Audio
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
from pyannote.core import Segment
import wave
import contextlib
from sklearn.cluster import AgglomerativeClustering
import numpy as np
# Load Whisper model
model_size = "medium.en"
model = whisper.load_model(model_size)
audio = Audio()
embedding_model = PretrainedSpeakerEmbedding("speechbrain/spkrec-ecapa-voxceleb", device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
def transcribe_and_diarize(audio_file, num_speakers=2):
try:
path = audio_file.name
# Convert to WAV if necessary
if not path.endswith('.wav'):
subprocess.call(['ffmpeg', '-i', path, 'audio.wav', '-y'])
path = 'audio.wav'
# Transcribe audio
result = model.transcribe(path)
segments = result["segments"]
# Get audio duration
with contextlib.closing(wave.open(path, 'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
# Define function to extract segment embeddings
def segment_embedding(segment):
start = segment["start"]
end = min(duration, segment["end"])
clip = Segment(start, end)
waveform, sample_rate = audio.crop(path, clip)
return embedding_model(waveform[None])
# Extract embeddings for each segment
embeddings = np.zeros(shape=(len(segments), 192))
for i, segment in enumerate(segments):
embeddings[i] = segment_embedding(segment)
embeddings = np.nan_to_num(embeddings)
# Perform speaker clustering
clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
labels = clustering.labels_
for i in range(len(segments)):
segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
# Generate transcript
transcript = ""
for i, segment in enumerate(segments):
if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
transcript += "\n" + segment["speaker"] + ' ' + str(datetime.timedelta(seconds=round(segment["start"]))) + '\n'
transcript += segment["text"][1:] + ' '
transcript += "\n\n"
return transcript
except Exception as e:
return f"An error occurred: {str(e)}"
iface = gr.Interface(
fn=transcribe_and_diarize,
inputs=[
gr.Audio(type="filepath", label="Upload Audio File"),
gr.Number(value=2, label="Number of Speakers")
],
outputs="text",
title="Audio Transcription and Speaker Diarization",
description="Upload an audio file to get a transcription with speaker diarization."
)
iface.launch()
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