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from datasets import load_dataset |
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from datasets import Audio |
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import numpy as np |
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from transformers import WhisperForConditionalGeneration, AutoProcessor, pipeline |
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import torch |
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from jiwer import wer |
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import whisper |
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PRECISION = torch.float16 |
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PRECISION = torch.float32 |
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DO_COND = True |
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model_id = "openai/whisper-tiny.en" |
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processor = AutoProcessor.from_pretrained(model_id) |
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model = WhisperForConditionalGeneration.from_pretrained(model_id, torch_dtype=PRECISION) |
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model = model.to("cuda") |
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model_orig = whisper.load_model(model_id.split("whisper-")[-1]) |
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ds = load_dataset("distil-whisper/earnings21", "full")["test"] |
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ds = ds.cast_column("audio", Audio(sampling_rate=16000)) |
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num_samples = 3 |
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start = 2 |
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audios = [x['array'] for x in ds[start:num_samples]["audio"]] |
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for name in ["text", "transcription"]: |
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if name in ds.column_names: |
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labels = ds[start:num_samples][name] |
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break |
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for audio, label in zip(audios, labels): |
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inputs = processor(audio, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000) |
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inputs = inputs.to("cuda", PRECISION) |
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if inputs["input_features"].shape[-1] < 3000: |
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continue |
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result = model_orig.transcribe(audio.astype(dtype=np.float32), condition_on_previous_text=DO_COND, temperature=0.0, logprob_threshold=None, compression_ratio_threshold=None, no_speech_threshold=None) |
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gen_length = 448 |
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result_hf = model.generate(**inputs, condition_on_prev_tokens=DO_COND, max_new_tokens=gen_length, return_timestamps=True) |
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decoded = processor.batch_decode(result_hf, skip_special_tokens=True) |
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result = model.generate(**inputs, condition_on_previous_tokens=False, max_new_tokens=gen_length, return_timestamps=True) |
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result_text_norm = processor.tokenizer._normalize(result["text"]) |
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decoded_norm = processor.tokenizer._normalize(decoded[0]) |
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label_norm = processor.tokenizer._normalize(label) |
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wer_orig = wer(label_norm, result_text_norm) |
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wer_hf = wer(label_norm, decoded_norm) |
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print("Cond:\n", decoded_norm) |
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print(50 * "-") |
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print("Orig Cond:\n", result_text_norm) |
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print(50 * "-") |
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print("Label:\n", label_norm) |
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print("Result:") |
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print("WER Orig", wer_orig) |
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print("WER HF", wer_hf) |
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print("Done") |
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