patrickvonplaten
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Update README.md

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  1. README.md +1 -8
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@@ -44,19 +44,12 @@ To transcribe audio files the model can be used as a standalone acoustic model a
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  # load model and tokenizer
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  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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-
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- # define function to read in sound file
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- def map_to_array(batch):
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- speech, _ = sf.read(batch["file"])
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- batch["speech"] = speech
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- return batch
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  # load dummy dataset and read soundfiles
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  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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- ds = ds.map(map_to_array)
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  # tokenize
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- input_values = processor(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1
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  # retrieve logits
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  logits = model(input_values).logits
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  # load model and tokenizer
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  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
 
 
 
 
 
 
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  # load dummy dataset and read soundfiles
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  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 
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  # tokenize
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+ input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
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  # retrieve logits
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  logits = model(input_values).logits