Wav2vec_cv Open-Source Speech Recognition Model - Achieving Accurate Speech Recognition through Fine-Tuning

Wav2vec Cv

Developed by eugenetanjc

A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Fine-tuned Model #Low Word Error Rate

Downloads 69

Release Time : 6/24/2022

Model Overview

This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unspecified dataset, primarily used for speech recognition tasks.

Model Features

Efficient Fine-tuning

Fine-tuned based on the pre-trained wav2vec2-base-960h model, improving recognition performance in specific scenarios

Stable Training

After 60 training epochs, the validation loss stabilized around 4.17

Model Capabilities

Speech Recognition

Audio to Text Conversion

Use Cases

Speech Transcription

Meeting Minutes

Convert meeting recordings into text transcripts

Voice Notes

Convert voice memos into text

🚀 wav2vec_cv

This model is a fine - tuned version of [facebook/wav2vec2 - base - 960h](https://huggingface.co/facebook/wav2vec2 - base - 960h) on the None dataset, achieving a loss of 4.1760 and a WER of 1.0 on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [facebook/wav2vec2 - base - 960h](https://huggingface.co/facebook/wav2vec2 - base - 960h) on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.1760
Wer: 1.0

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.003
train_batch_size: 6
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
7.1467	4.29	30	4.2173	1.0
6.8918	8.57	60	4.2004	1.0
5.4913	12.86	90	4.2007	1.0
5.3906	17.14	120	4.1765	1.0
4.9212	21.43	150	4.1714	1.0
4.3916	25.71	180	4.1811	1.0
5.2255	30.0	210	4.1633	1.0
4.501	34.29	240	4.2050	1.0
4.4328	38.57	270	4.1572	1.0
4.2136	42.86	300	4.1698	1.0
4.3353	47.14	330	4.1721	1.0
4.1805	51.43	360	4.1804	1.0
4.1695	55.71	390	4.1801	1.0
4.2978	60.0	420	4.1760	1.0