---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Wav2Vec2 Large 53 Portugese by Gunjan Chhablani
  results:
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice pt
      type: common_voice
      args: pt
    metrics:
       - name: Test WER
         type: wer
         value: {wer_result_on_test}
---

# Wav2Vec2-Large-XLSR-53-Portuguese

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. 
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
  \tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
  \tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation

The model can be evaluated as follows on the Portuguese test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "pt", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\'\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\t  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\t\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
  \tbatch["speech"] = resampler(speech_array).squeeze().numpy()
  \treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
  \tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

  \twith torch.no_grad():
  \t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

  \tpred_ids = torch.argmax(logits, dim=-1)
  \tbatch["pred_strings"] = processor.batch_decode(pred_ids)
  \treturn batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: XX.XX %  # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.


## Training

The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py).
 The parameters passed were:

```bash
#!/usr/bin/env bash
python run_common_voice.py \\
    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
    --dataset_config_name="pt" \\
    --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \\
    --cache_dir=/workspace/data \\
    --overwrite_output_dir \\
    --num_train_epochs="30" \\
    --per_device_train_batch_size="32" \\
    --per_device_eval_batch_size="32" \\
    --evaluation_strategy="steps" \\
    --learning_rate="3e-4" \\
    --warmup_steps="500" \\
    --fp16 \\
    --freeze_feature_extractor \\
    --save_steps="500" \\
    --eval_steps="500" \\
    --save_total_limit="1" \\
    --logging_steps="500" \\
    --group_by_length \\
    --feat_proj_dropout="0.0" \\
    --layerdrop="0.1" \\
    --gradient_checkpointing \\
    --do_train --do_eval \\
```