Downloads · 30 days
16
2% of all-time downloads
xls-r-300m-cv_8-fr
Plim/xls-r-300m-cv_8-fr
xls-r-300m-cv_8-fr is a automatic speech recognition model from Plim. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMONVOICE80 - FR dataset.
All-time downloads
788
Public
Repo size
41.4 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.3 GB · 85%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 9, 2022
- SHA
- 799632eb
Model card
From the Hugging Face model README
Model description
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0 (extended to 7.0 with training with checkpoint)
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.9114 | 0.29 | 1000 | inf | 0.9997 |
| 1.2436 | 0.57 | 2000 | inf | 0.4310 |
| 1.0552 | 0.86 | 3000 | inf | 0.3144 |
| 1.0044 | 1.15 | 4000 | inf | 0.2814 |
| 0.9718 | 1.43 | 5000 | inf | 0.2658 |
| 0.9502 | 1.72 | 6000 | inf | 0.2566 |
| 0.9418 | 2.01 | 7000 | inf | 0.2476 |
| 0.9215 | 2.29 | 8000 | inf | 0.2420 |
| 0.9236 | 2.58 | 9000 | inf | 0.2388 |
| 0.9014 | 2.87 | 10000 | inf | 0.2354 |
| 0.8814 | 3.15 | 11000 | inf | 0.2312 |
| 0.8809 | 3.44 | 12000 | inf | 0.2285 |
| 0.8717 | 3.73 | 13000 | inf | 0.2263 |
| 0.8787 | 4.01 | 14000 | inf | 0.2218 |
| 0.8567 | 4.3 | 15000 | inf | 0.2193 |
| 0.8488 | 4.59 | 16000 | inf | 0.2187 |
| 0.8359 | 4.87 | 17000 | inf | 0.2172 |
Training continued with checkpoint from STEP 17000:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| / | 5.16 | 18000 | inf | 0.2176 |
| / | 5.45 | 19000 | inf | 0.2181 |
| / | 5.73 | 20000 | inf | 0.2155 |
| / | 6.02 | 21000 | inf | 0.2140 |
| / | 6.31 | 22000 | inf | 0.2124 |
| / | 6.59 | 23000 | inf | 0.2117 |
| / | 6.88 | 24000 | inf | 0.2116 |
It achieves the best result on the validation set on Step 24000:
- Wer: 0.2116
Got some issue with validation loss calculation.
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8with splittest
python eval.py --model_id Plim/xls-r-300m-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id Plim/xls-r-300m-cv_8-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Languages
- fr
- Created
- Mar 2, 2022
- Updated
- Feb 9, 2022