nguyenvulebinh/iwslt-asr-wav2vec-large-4500h

Fine-Tune Wav2Vec2 large model for English ASR

Data for fine-tune

Dataset	Duration in hours
Common Voice	1667
Europarl	85
How2	356
Librispeech	936
MuST-C v1	407
MuST-C v2	482
Tedlium	482

Evaluation result

Dataset	Duration in hours	WER w/o LM	WER with LM
Librispeech	5.4	2.9	1.1
Tedlium	2.6	7.9	5.4

Usage

from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
from transformers import Wav2Vec2ProcessorWithLM
from IPython.lib.display import Audio
import torchaudio
import torch

# Load model & processor
model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large-4500h"
model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)

# Load an example audio (16k)
audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')

# Infer
output = model(**input_data)

# Output transcript without LM
print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten

# Output transcript with LM
print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten

Model Parameters License

The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

Contact

[email protected]