fix more

hf_whisper_meanwhile.py

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+#!/usr/bin/env python3
+from datasets import load_dataset
+from datasets import Audio
+import numpy as np
+from transformers import WhisperForConditionalGeneration, AutoProcessor, pipeline
+import torch
+from jiwer import wer
+import whisper
+
+PRECISION = torch.float16
+PRECISION = torch.float32
+DO_COND = True
+
+# model_id = "openai/whisper-tiny"
+model_id = "openai/whisper-tiny.en"
+# model_id = "openai/whisper-large-v2"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = WhisperForConditionalGeneration.from_pretrained(model_id, torch_dtype=PRECISION)
+model = model.to("cuda")
+
+model_orig = whisper.load_model(model_id.split("whisper-")[-1])
+
+# ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean")
+# ds = load_dataset("distil-whisper/meanwhile", "default")["test"]
+ds = load_dataset("distil-whisper/earnings21", "full")["test"]
+ds = ds.cast_column("audio", Audio(sampling_rate=16000))
+
+num_samples = 3
+start = 2
+
+audios = [x['array'] for x in ds[start:num_samples]["audio"]]
+for name in ["text", "transcription"]:
+    if name in ds.column_names:
+        labels = ds[start:num_samples][name]
+        break
+
+for audio, label in zip(audios, labels):
+    inputs = processor(audio, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000)
+    inputs = inputs.to("cuda", PRECISION)
+
+    if inputs["input_features"].shape[-1] < 3000:
+        continue
+
+    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)
+
+    gen_length = 448
+    result_hf = model.generate(**inputs, condition_on_prev_tokens=DO_COND, max_new_tokens=gen_length, return_timestamps=True)
+    decoded = processor.batch_decode(result_hf, skip_special_tokens=True)
+
+    # result = model.generate(**inputs, condition_on_previous_tokens=False, max_new_tokens=gen_length, return_timestamps=True)
+    # decoded_2 = processor.batch_decode(result)
+    # print(50 * "-")
+
+    # result_2 = model_orig.transcribe(audio.astype(dtype=np.float32), condition_on_previous_text=False, temperature=0.0, logprob_threshold=None, compression_ratio_threshold=None, no_speech_threshold=None)
+    
+    result_text_norm = processor.tokenizer._normalize(result["text"])
+    decoded_norm = processor.tokenizer._normalize(decoded[0])
+    label_norm = processor.tokenizer._normalize(label)
+
+
+    wer_orig = wer(label_norm, result_text_norm)
+    wer_hf = wer(label_norm, decoded_norm)
+
+    print("Cond:\n", decoded_norm)
+    print(50 * "-")
+    # print("Not cond:\n", decoded_2)
+    # print(50 * "-")
+    print("Orig Cond:\n", result_text_norm)
+    print(50 * "-")
+    # print("Orig Not cond:\n", [result_2["text"]])
+    # print(50 * "=")
+    print("Label:\n", label_norm)
+
+    # break
+    #
+    print("Result:")
+    print("WER Orig", wer_orig)
+    print("WER HF", wer_hf)
+
+    print("Done")