main.py

import whisperx
import gc 

device = "cuda" 
audio_file = "Brand Media Works AI Chatbot Call Recording Sample1.mp3"
batch_size = 1 # reduce if low on GPU mem
compute_type = " --model base --output_format txt --compute_type int8" # change to "int8" if low on GPU mem (may reduce accuracy)

# 1. Transcribe with original whisper (batched)
model = whisperx.load_model("large-v2", device, compute_type=compute_type)

audio = whisperx.load_audio(audio_file)
result = model.transcribe(audio, batch_size=batch_size)
print(result["segments"]) # before alignment

# delete model if low on GPU resources
# import gc; gc.collect(); torch.cuda.empty_cache(); del model

# 2. Align whisper output
model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
result = whisperx.align(result["segments"], model_a, metadata, audio, device, return_char_alignments=False)

print(result["segments"]) # after alignment

# delete model if low on GPU resources
# import gc; gc.collect(); torch.cuda.empty_cache(); del model_a

# 3. Assign speaker labels
diarize_model = whisperx.DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)

# add min/max number of speakers if known
diarize_segments = diarize_model(audio_file)
# diarize_model(audio_file, min_speakers=min_speakers, max_speakers=max_speakers)

result = whisperx.assign_word_speakers(diarize_segments, result)
print(diarize_segments)
print(result["segments"]) # segments are now assigned speaker IDs