đ wav2vec2-large-xls-r-1b-cv8-mt
This model is a fine - tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. It effectively addresses the problem of automatic speech recognition in the Maltese language, offering high - quality speech - to - text conversion services.
đ Quick Start
This section provides a basic introduction to the model. The model is a fine - tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset.
⨠Features
- Fine - tuned from facebook/wav2vec2-xls-r-1b on the common_voice dataset.
- Achieves specific results on the evaluation set, including a Loss of 0.2210 and a Wer of 0.1974.
- Another version with a KenLM 3 - gram model is available, which performs better.
đ Documentation
Model description
Note: another version of this model is available with a KenLM 3 - gram 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
đ License
The model is licensed under the Apache - 2.0 license.
Property |
Details |
Model Type |
Fine - tuned model on common_voice dataset based on facebook/wav2vec2-xls-r-1b |
Training Data |
mozilla - foundation/common_voice_8_0 |
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 |