patrickvonplaten
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Update README.md

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  1. README.md +3 -3
README.md CHANGED
@@ -36,13 +36,13 @@ The original model can be found under https://github.com/pytorch/fairseq/tree/ma
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  To transcribe audio files the model can be used as a standalone acoustic model as follows:
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  ```python
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- from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
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  from datasets import load_dataset
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  import soundfile as sf
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  import torch
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  # load model and tokenizer
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- tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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  # define function to read in sound file
@@ -81,7 +81,7 @@ from jiwer import wer
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  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda")
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- tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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  def map_to_array(batch):
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  speech, _ = sf.read(batch["file"])
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  To transcribe audio files the model can be used as a standalone acoustic model as follows:
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  ```python
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+ from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC
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  from datasets import load_dataset
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  import soundfile as sf
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  import torch
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  # load model and tokenizer
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+ tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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  # define function to read in sound file
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  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
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  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda")
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+ tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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  def map_to_array(batch):
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  speech, _ = sf.read(batch["file"])