
# Wav2Vec2-Large-XLSR-53-Thai

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Thai using the [Common Voice](https://huggingface.co/datasets/common_voice)
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
from pythainlp.tokenize import word_tokenize

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

processor = Wav2Vec2Processor.from_pretrained("sakares/wav2vec2-large-xlsr-thai-demo")
model = Wav2Vec2ForCTC.from_pretrained("sakares/wav2vec2-large-xlsr-thai-demo")

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

## For Thai NLP Library, please feel free to check https://pythainlp.github.io/docs/2.2/api/tokenize.html
def th_tokenize(batch):
    batch["sentence"] = " ".join(word_tokenize(batch["sentence"], engine="newmm"))
    return batch

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

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

with torch.no_grad():
	logits = 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])
```
Usage script [here](https://colab.research.google.com/drive/1w0VywsBtjrO2pHHPmiPugYI9yeF8nUKg?usp=sharing)

## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from pythainlp.tokenize import word_tokenize
import re

test_dataset = load_dataset("common_voice", "th", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("sakares/wav2vec2-large-xlsr-thai-demo")
model = Wav2Vec2ForCTC.from_pretrained("sakares/wav2vec2-large-xlsr-thai-demo")
model.to("cuda")

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

## For Thai NLP Library, please feel free to check https://pythainlp.github.io/docs/2.2/api/tokenize.html
def th_tokenize(batch):
    batch["sentence"] = " ".join(word_tokenize(batch["sentence"], engine="newmm"))
    return batch

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

test_dataset = test_dataset.map(speech_file_to_array_fn).map(th_tokenize)

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

	with torch.no_grad():
		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

	pred_ids = torch.argmax(logits, dim=-1)
	batch["pred_strings"] = processor.batch_decode(pred_ids)
	return 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**: 44.46 %
Evaluate script [here](https://colab.research.google.com/drive/1WZGtHKWXBztRsuXHIdebf6uoAsp7rTnK?usp=sharing)

## Training

The Common Voice `train`, `validation` datasets were used for training.

The script used for training can be found [here](https://colab.research.google.com/drive/18oUbeZgBGSkz16zC_WOa154QZOdmvjyt?usp=sharing)

{"language": "th", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Large Thai by Sakares", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice th", "type": "common_voice", "args": "th"}, "metrics": [{"type": "wer", "value": 44.46, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week th dataset:common_voice license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #th #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


# Wav2Vec2-Large-XLSR-53-Thai

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Common Voice
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:


Usage script here

## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 44.46 %
Evaluate script here

## Training

The Common Voice 'train', 'validation' datasets were used for training.

The script used for training can be found here

# Wav2Vec2-Large-XLSR-53-Thai

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Common Voice
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:


Usage script here ## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 44.46 %
Evaluate script here ## Training

The Common Voice 'train', 'validation' datasets were used for training.

The script used for training can be found here
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #th #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Wav2Vec2-Large-XLSR-53-Thai

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Common Voice
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:


Usage script here ## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 44.46 %
Evaluate script here ## Training

The Common Voice 'train', 'validation' datasets were used for training.

The script used for training can be found here
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #th #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Wav2Vec2-Large-XLSR-53-Thai

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Common Voice
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:


Usage script here## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 44.46 %
Evaluate script here## Training

The Common Voice 'train', 'validation' datasets were used for training.

The script used for training can be found here