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wav2vec2-large-960h-lv60-self
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Update README.md

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Patrick von Platen 2021-02-09 09:11:54 +00:00 committed by huggingface-web
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@ -30,14 +30,14 @@ The original model can be found under https://github.com/pytorch/fairseq/tree/ma
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForMaskedLM
from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import torch
# load model and tokenizer
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
# define function to read in sound file
def map_to_array(batch):
@ -66,7 +66,7 @@ To transcribe audio files the model can be used as a standalone acoustic model a
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForMaskedLM, Wav2Vec2Tokenizer
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import soundfile as sf
import torch
from jiwer import wer
@ -74,7 +74,7 @@ from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h-lv60-self")
def map_to_array(batch):