whisperx_test.py

import whisperx
import gc

device = "cuda"
audio_file = "audio.mp3"
batch_size = 16
language = "en"
compute_type = "float16"

with open("hf_token.txt", "r") as f:
    HF_TOKEN = f.read()

# 1. Transcribe with original whisper (batched)
# save model to local path (optional)
model_dir = "./models/"
model = whisperx.load_model("large-v2", device, language="en", compute_type=compute_type, download_root=model_dir)

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=HF_TOKEN, device=device)

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