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# 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

{"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": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "mt"}, "metrics": [{"type": "wer", "value": 17.57, "name": "Test WER"}, {"type": "cer", "value": 3.86, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "mt"}, "metrics": [{"type": "wer", "name": "Test WER"}, {"type": "cer", "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition mozilla-foundation/common_voice_8_0 generated_from_trainer mt robust-speech-event model_for_talk hf-asr-leaderboard dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

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 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 URL


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



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



### 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



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
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### 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



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
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### 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")