Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Vietnamese
wav2vec2-bert
mozilla-foundation/common_voice_16_0
Generated from Trainer
Eval Results (legacy)
Instructions to use ylacombe/wav2vec2-common_voice-vi-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ylacombe/wav2vec2-common_voice-vi-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ylacombe/wav2vec2-common_voice-vi-demo")# Load model directly from transformers import AutoProcessor, Wav2Vec2BERTForCTC processor = AutoProcessor.from_pretrained("ylacombe/wav2vec2-common_voice-vi-demo") model = Wav2Vec2BERTForCTC.from_pretrained("ylacombe/wav2vec2-common_voice-vi-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-common_voice-vi-demo
This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - VI dataset. It achieves the following results on the evaluation set:
- Loss: 3.3958
- Wer: 1.0
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:
- learning_rate: 0.002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 2.26 | 200 | 3.5924 | 1.0 |
| No log | 4.52 | 400 | 3.4946 | 1.0 |
| 5.7152 | 6.78 | 600 | 3.4630 | 1.0 |
| 5.7152 | 9.04 | 800 | 3.4525 | 1.0 |
| 3.5048 | 11.3 | 1000 | 3.4329 | 1.0 |
| 3.5048 | 13.56 | 1200 | 3.4074 | 1.0 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.15.0
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Model tree for ylacombe/wav2vec2-common_voice-vi-demo
Base model
ylacombe/w2v-bert-2.0Evaluation results
- Wer on MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - VItest set self-reported1.000