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

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Patrick von Platen 2021-08-27 15:37:00 +00:00 committed by huggingface-web
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@ -31,13 +31,13 @@ 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, Wav2Vec2ForCTC
from transformers import Wav2Vec2Processor, 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")
# load model and processor
processor = Wav2Vec2Processor.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
@ -51,14 +51,14 @@ To transcribe audio files the model can be used as a standalone acoustic model a
ds = ds.map(map_to_array)
# tokenize
input_values = tokenizer(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1
input_values = processor(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = tokenizer.batch_decode(predicted_ids)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
@ -67,7 +67,7 @@ To transcribe audio files the model can be used as a standalone acoustic model a
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import soundfile as sf
import torch
from jiwer import wer
@ -76,7 +76,7 @@ from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
@ -86,7 +86,7 @@ def map_to_array(batch):
librispeech_eval = librispeech_eval.map(map_to_array)
def map_to_pred(batch):
inputs = tokenizer(batch["speech"], return_tensors="pt", padding="longest")
inputs = processor(batch["speech"], return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
@ -94,7 +94,7 @@ def map_to_pred(batch):
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = tokenizer.batch_decode(predicted_ids)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch