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| import tensorflow as tf | |
| from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration | |
| from datasets import load_dataset | |
| import soundfile as sf | |
| model = TFSpeech2TextForConditionalGeneration.from_pretrained( | |
| "facebook/s2t-small-librispeech-asr", from_pt=True | |
| ) | |
| processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") | |
| def map_to_array(batch): | |
| speech, _ = sf.read(batch["file"]) | |
| batch["speech"] = speech | |
| return batch | |
| ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| ds = ds.map(map_to_array) | |
| ds.set_format(type="tf") | |
| input_features = processor( | |
| ds["speech"][0], sampling_rate=16000, return_tensors="tf" | |
| ).input_features # Batch size 1 | |
| generated_ids = model.generate(input_features) | |
| transcription = processor.batch_decode(generated_ids) |