Open-source Wav2Vec2 Model - Efficiently Recognize Scottish-accented Speech and Provide Precise Support for Communication Scenarios

Wav2vec2 Common Voice Accents Scotland

Developed by willcai

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset, specializing in Scottish accent speech recognition.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Scottish accent recognition #Speech-to-text #Multi-GPU training

Downloads 19

Release Time : 3/22/2022

Model Overview

This is a speech recognition model optimized for Scottish accents, based on the wav2vec2-xls-r-300m architecture and fine-tuned on the common_voice dataset.

Model Features

Scottish accent optimization

Specifically optimized for Scottish accents to improve recognition accuracy for this accent

Based on wav2vec2-xls-r architecture

Utilizes the powerful wav2vec2-xls-r-300m pre-trained model as the foundation

Multi-GPU training

Uses 8 GPUs for distributed training to enhance training efficiency

Model Capabilities

Speech recognition

Scottish accent recognition

English speech transcription

Use Cases

Speech transcription

Scottish accent speech transcription

Transcribe English speech with Scottish accents into text

Validation loss 0.2752

Voice assistants

Voice assistant for Scottish regions

Provide more accurate voice interaction experiences for users in Scottish regions

🚀 wav2vec2_common_voice_accents_scotland

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m) on the common_voice dataset. It can be used for speech - related tasks. On the evaluation set, it achieves a loss of 0.2752, indicating its effectiveness in the relevant tasks.

🚀 Quick Start

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m) on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.2752

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Property	Details
learning_rate	0.0003
train_batch_size	48
eval_batch_size	4
seed	42
distributed_type	multi - GPU
num_devices	8
total_train_batch_size	384
total_eval_batch_size	32
optimizer	Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type	linear
lr_scheduler_warmup_steps	500
num_epochs	30
mixed_precision_training	Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.7171	1.28	400	1.1618
0.4391	2.56	800	0.2422
0.2259	3.83	1200	0.2071
0.1813	5.11	1600	0.2126
0.1531	6.39	2000	0.2010
0.1383	7.67	2400	0.2004
0.13	8.95	2800	0.2069
0.1193	10.22	3200	0.2081
0.1124	11.5	3600	0.2051
0.1023	12.78	4000	0.2175
0.097	14.06	4400	0.2261
0.0863	15.34	4800	0.2301
0.0823	16.61	5200	0.2334
0.079	17.89	5600	0.2252
0.0743	19.17	6000	0.2393
0.0696	20.45	6400	0.2481
0.0644	21.73	6800	0.2416
0.064	23.0	7200	0.2449
0.0584	24.28	7600	0.2660
0.0544	25.56	8000	0.2630
0.0523	26.84	8400	0.2677
0.0494	28.12	8800	0.2730
0.0462	29.39	9200	0.2752

Framework versions

Transformers 4.17.0
Pytorch 1.10.2+cu102
Datasets 1.18.4
Tokenizers 0.11.6

📄 License

This model is licensed under the Apache 2.0 license.

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