Edit model card

Wav2Vec2-Base-960h

This repository is a reimplementation of official Facebook’s wav2vec. There is no description of converting the wav2vec pretrain model to a pytorch.bin file. We are rebuilding pytorch.bin from the pretrain model. Here is the conversion method.

pip install transformers[sentencepiece]
pip install fairseq -U

git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .

wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_960h.pt -O ./wav2vec_small_960h.pt
mkdir dict
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt

mkdir outputs
python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./wav2vec_small_960h.pt --dict_path ./dict

Usage

To transcribe audio files the model can be used as a standalone acoustic model as follows:

 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-base-960h")
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
 
 # define function to read in sound file
 def map_to_array(batch):
     speech, _ = sf.read(batch["file"])
     batch["speech"] = speech
     return batch
     
 # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 ds = ds.map(map_to_array)
 
 # tokenize
 input_values = tokenizer(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)

Evaluation

This code snippet shows how to evaluate facebook/wav2vec2-base-960h on LibriSpeech's "clean" and "other" test data.

from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import soundfile as sf
import torch
from jiwer import wer


librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")

model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda")
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")

def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

librispeech_eval = librispeech_eval.map(map_to_array)

def map_to_pred(batch):
    input_values = tokenizer(batch["speech"], return_tensors="pt", padding="longest").input_values
    with torch.no_grad():
        logits = model(input_values.to("cuda")).logits

    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = tokenizer.batch_decode(predicted_ids)
    batch["transcription"] = transcription
    return batch

result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])

print("WER:", wer(result["text"], result["transcription"]))

Result (WER):

"clean" "other"
3.4 8.6

Reference

Facebook's Wav2Vec2

Facebook's huggingface Wav2Vec2

Paper

Downloads last month
9
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train tommy19970714/wav2vec2-base-960h