
# Wav2Vec2-Large-XLSR-53-Swahili 

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swahili using the following datasets:
- [ALFFA](http://www.openslr.org/25/),
- [Gamayun](https://gamayun.translatorswb.org/download/gamayun-5k-english-swahili/) 
- [IWSLT](https://iwslt.org/2021/low-resource)

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


processor = Wav2Vec2Processor.from_pretrained("alokmatta/wav2vec2-large-xlsr-53-sw")

model = Wav2Vec2ForCTC.from_pretrained("alokmatta/wav2vec2-large-xlsr-53-sw").to("cuda")

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

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

def load_file_to_data(file):
    batch = {}
    speech, _ = torchaudio.load(file)
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    return batch


def predict(data):
    features = processor(data["speech"], sampling_rate=data["sampling_rate"], padding=True, return_tensors="pt")
    input_values = features.input_values.to("cuda")
    attention_mask = features.attention_mask.to("cuda")
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
    pred_ids = torch.argmax(logits, dim=-1)
    return processor.batch_decode(pred_ids)

predict(load_file_to_data('./demo.wav'))
```

**Test Result**: 40 % 


## Training


The script used for training can be found [here](https://colab.research.google.com/drive/1_RL6TQv_Yiu_xbWXu4ycbzdCdXCqEQYU?usp=sharing)
{"language": "sw", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["ALFFA,Gamayun & IWSLT"], "metrics": ["wer"]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week sw license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sw #license-apache-2.0 #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Swahili 

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:
- ALFFA,
- Gamayun 
- IWSLT

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:



Test Result: 40 % 


## Training


The script used for training can be found here
# Wav2Vec2-Large-XLSR-53-Swahili 

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:
- ALFFA,
- Gamayun 
- IWSLT

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:



Test Result: 40 % ## Training


The script used for training can be found here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sw #license-apache-2.0 #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Swahili 

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:
- ALFFA,
- Gamayun 
- IWSLT

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:



Test Result: 40 % ## Training


The script used for training can be found here
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sw #license-apache-2.0 #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Swahili 

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swahili using the following datasets:
- ALFFA,
- Gamayun 
- IWSLT

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:



Test Result: 40 %## Training


The script used for training can be found here