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stf/test.py

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+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)