wav2vec2-train / README.md

Vikas Bhandari

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	---
	language: en
	datasets:
	- librispeech_asr
	tags:
	- speech
	- audio
	- automatic-speech-recognition
	- hf-asr-leaderboard
	license: apache-2.0
	model-index:
	- name: wav2vec2-large-960h-lv60
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: LibriSpeech (clean)
	type: librispeech_asr
	config: clean
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 1.9
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: LibriSpeech (other)
	type: librispeech_asr
	config: other
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 3.9
	---

	# Wav2Vec2-Large-960h-Lv60 + Self-Training

	[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)

	The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz.

	[Paper](https://arxiv.org/abs/2006.11477)

	Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli

	Abstract

	We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

	The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.


	# Usage

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

	```python
	from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
	from datasets import load_dataset
	import torch

	# load model and processor
	processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
	model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")

	# load dummy dataset and read soundfiles
	ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")

	# tokenize
	input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values

	# retrieve logits
	logits = model(input_values).logits

	# take argmax and decode
	predicted_ids = torch.argmax(logits, dim=-1)
	transcription = processor.batch_decode(predicted_ids)
	```

	## Evaluation

	This code snippet shows how to evaluate facebook/wav2vec2-large-960h-lv60-self on LibriSpeech's "clean" and "other" test data.

	```python
	from datasets import load_dataset
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import torch
	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")
	processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")

	def map_to_pred(batch):
	inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest")
	input_values = inputs.input_values.to("cuda")
	attention_mask = inputs.attention_mask.to("cuda")

	with torch.no_grad():
	logits = model(input_values, attention_mask=attention_mask).logits

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

	result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])

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

	Result (WER):

	\| "clean" \| "other" \|
	\|---\|---\|
	\| 1.9 \| 3.9 \|