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# wav2vec2-large-xls-r-300m-as

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9068
- Wer: 0.6679

## 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: 0.0003
- 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_ratio: 0.12
- num_epochs: 240

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.7027        | 21.05  | 400  | 3.4157          | 1.0    |
| 1.1638        | 42.1   | 800  | 1.3498          | 0.7461 |
| 0.2266        | 63.15  | 1200 | 1.6147          | 0.7273 |
| 0.1473        | 84.21  | 1600 | 1.6649          | 0.7108 |
| 0.1043        | 105.26 | 2000 | 1.7691          | 0.7090 |
| 0.0779        | 126.31 | 2400 | 1.8300          | 0.7009 |
| 0.0613        | 147.36 | 2800 | 1.8681          | 0.6916 |
| 0.0471        | 168.41 | 3200 | 1.8567          | 0.6875 |
| 0.0343        | 189.46 | 3600 | 1.9054          | 0.6840 |
| 0.0265        | 210.51 | 4000 | 1.9020          | 0.6786 |
| 0.0219        | 231.56 | 4400 | 1.9068          | 0.6679 |


### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3

#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`

```bash
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-as --dataset mozilla-foundation/common_voice_7_0 --config as --split test
```


### Inference With LM

```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-as"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "as", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
    logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "জাহাজত তো তিশকুৰলৈ যাব কিন্তু জহাজিটো আহিপনে"
```

### Eval results on Common Voice 7 "test" (WER):

| Without LM | With LM (run `./eval.py`) |
|---|---|
| 67 | 56.995 |


{"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "as"}, "metrics": [{"type": "wer", "value": 56.995, "name": "Test WER"}, {"type": "cer", "value": 20.39, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition hf-asr-leaderboard robust-speech-event as dataset:mozilla-foundation/common_voice_7_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #as #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-as
============================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:


* Loss: 1.9068
* Wer: 0.6679


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: 0.0003
* 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\_ratio: 0.12
* num\_epochs: 240


### Training results



### Framework versions


* Transformers 4.16.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3


#### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'


### Inference With LM


### Eval results on Common Voice 7 "test" (WER):




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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\_ratio: 0.12
* num\_epochs: 240 ### Training results ### Framework versions


* Transformers 4.16.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3 #### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test' ### Inference With LM ### Eval results on Common Voice 7 "test" (WER):
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #as #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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\_ratio: 0.12
* num\_epochs: 240 ### Training results ### Framework versions


* Transformers 4.16.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3 #### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test' ### Inference With LM ### Eval results on Common Voice 7 "test" (WER):
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #as #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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\_ratio: 0.12
* num\_epochs: 240### Training results### Framework versions


* Transformers 4.16.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3#### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'### Inference With LM### Eval results on Common Voice 7 "test" (WER):