| from time import time | |
| from datasets import load_dataset | |
| from faster_whisper import WhisperModel | |
| # from transformers import WhisperForConditionalGeneration, WhisperProcessor | |
| ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", cache_dir=".") | |
| # processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3") | |
| # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3").to("mps") | |
| model = WhisperModel("large-v3", device="cuda", compute_type="float16", download_root=".") | |
| audio_sample = ds[0]["audio"] | |
| waveform = audio_sample["array"] | |
| sampling_rate = audio_sample["sampling_rate"] | |
| tic = time() | |
| # input_features = processor( | |
| # waveform, sampling_rate=sampling_rate, return_tensors="pt" | |
| # ).input_features | |
| segments, info = model.transcribe(waveform, beam_size=5) | |
| # predicted_ids = model.generate(input_features.to("mps")) | |
| # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| toc = time() | |
| # print(transcription[0]) | |
| for segment in segments: | |
| print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) | |
| print(toc - tic) |