|
--- |
|
language: |
|
- mt |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- mozilla-foundation/common_voice_8_0 |
|
- generated_from_trainer |
|
- mt |
|
- robust-speech-event |
|
- model_for_talk |
|
- hf-asr-leaderboard |
|
datasets: |
|
- mozilla-foundation/common_voice_8_0 |
|
model-index: |
|
- name: wav2vec2-large-xls-r-1b-cv8-mt |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice 8 |
|
type: mozilla-foundation/common_voice_8_0 |
|
args: mt |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 17.57 |
|
- name: Test CER |
|
type: cer |
|
value: 3.86 |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Dev Data |
|
type: speech-recognition-community-v2/dev_data |
|
args: mt |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: null |
|
- name: Test CER |
|
type: cer |
|
value: null |
|
--- |
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# wav2vec2-large-xls-r-1b-cv8-mt |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2210 |
|
- Wer: 0.1974 |
|
|
|
## Model description |
|
|
|
Note: another version of this model is available with a KenLM 3gram model. This model performs better than this model. See https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt-lm |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following config and hyperparameters were used during training: |
|
|
|
model = Wav2Vec2ForCTC.from_pretrained( |
|
"facebook/wav2vec2-xls-r-1b", |
|
attention_dropout=0.05, |
|
hidden_dropout=0.05, |
|
feat_proj_dropout=0.05, |
|
mask_time_prob=0.55, |
|
mask_feature_prob=0.10, |
|
layerdrop=0.05, |
|
ctc_zero_infinity=True, |
|
ctc_loss_reduction="mean", |
|
pad_token_id=processor.tokenizer.pad_token_id, |
|
vocab_size=len(processor.tokenizer), |
|
) |
|
|
|
from transformers import TrainingArguments |
|
|
|
training_args = TrainingArguments( |
|
output_dir=repo_name, |
|
group_by_length=True, |
|
per_device_train_batch_size=32, |
|
gradient_accumulation_steps=2, |
|
evaluation_strategy="steps", |
|
num_train_epochs=50, |
|
gradient_checkpointing=True, |
|
fp16=True, |
|
save_steps=400, |
|
eval_steps=400, |
|
logging_steps=400, |
|
learning_rate=5.5e-05, |
|
warmup_steps=500, |
|
save_total_limit=2, |
|
push_to_hub=True, |
|
report_to="tensorboard") |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 3.4564 | 13.33 | 400 | 0.3783 | 0.3981 | |
|
| 0.7931 | 26.66 | 800 | 0.2377 | 0.2298 | |
|
| 0.5364 | 39.98 | 1200 | 0.2210 | 0.1974 | |
|
|
|
Note that the test WER of 19.74 is different than the above reported 17.57. This was due to a bug which was found while processing files with an older version of the datasets library. The right library is listed below. |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0.dev0 |
|
- Pytorch 1.10.2+cu102 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.0 |
|
|
