---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-ft-ccv-en-cy
  results: []
datasets:
- techiaith/commonvoice_16_1_en_cy
language:
- cy
- en
pipeline_tag: automatic-speech-recognition
---

# wav2vec2-xlsr-53-ft-cy-en-withlm

This model is a version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
that has been fined-tuned with a custom bilingual datasets derived from the Welsh 
and English data releases of Mozilla Foundation's Commonvoice project. See : [techiaith/commonvoice_16_1_en_cy](https://huggingface.co/datasets/techiaith/commonvoice_16_1_en_cy).

In addition, this model also includes a single KenLM n-gram model trained with balanced 
collections of Welsh and English texts from [OSCAR](https://huggingface.co/datasets/oscar) 
This avoids the need for any language detection for determining whether to use a Welsh or English n-gram models during CTC decoding. 


## Usage

The `wav2vec2-xlsr-53-ft-cy-en-withlm` model can be used directly as follows:

```python
import torch
import torchaudio
import librosa

from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM

processor = Wav2Vec2ProcessorWithLM.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm")
model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm")

audio, rate = librosa.load(<path/to/audio_file>, sr=16000)

inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True)

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

print("Prediction: ", processor.batch_decode(tlogits.numpy(), beam_width=10).text[0].strip())

```

Usage with a pipeline is even simpler...

```
from transformers import pipeline

transcriber = pipeline("automatic-speech-recognition", model="techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm")

def transcribe(audio):
    return transcriber(audio)["text"]

transcribe(<path/or/url/to/any/audiofile>)
```


## Evaluation


According to a balanced English+Welsh test set derived from Common Voice version 16.1, the WER of techiaith/wav2vec2-xlsr-53-ft-cy-en-withlm is **23.79%**

However, when evaluated with language specific test sets, the model exhibits a bias to perform better with Welsh.  

| Common Voice Test Set Language | WER | CER | 
| -------- | --- | --- | 
| EN+CY | 23.79| 9.68  | 
| EN | 34.47  | 14.83  |
| CY | 12.34  | 3.55  |


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- training_steps: 9000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.0574        | 0.25  | 500  | 2.0297          | 0.9991 |
| 1.224         | 0.5   | 1000 | 0.5368          | 0.4342 |
| 0.434         | 0.75  | 1500 | 0.4861          | 0.3891 |
| 0.3295        | 1.01  | 2000 | 0.4301          | 0.3411 |
| 0.2739        | 1.26  | 2500 | 0.3818          | 0.3053 |
| 0.2619        | 1.51  | 3000 | 0.3894          | 0.3060 |
| 0.2517        | 1.76  | 3500 | 0.3497          | 0.2802 |
| 0.2244        | 2.01  | 4000 | 0.3519          | 0.2792 |
| 0.1854        | 2.26  | 4500 | 0.3376          | 0.2718 |
| 0.1779        | 2.51  | 5000 | 0.3206          | 0.2520 |
| 0.1749        | 2.77  | 5500 | 0.3169          | 0.2535 |
| 0.1636        | 3.02  | 6000 | 0.3122          | 0.2465 |
| 0.137         | 3.27  | 6500 | 0.3054          | 0.2382 |
| 0.1311        | 3.52  | 7000 | 0.2956          | 0.2280 |
| 0.1261        | 3.77  | 7500 | 0.2898          | 0.2236 |
| 0.1187        | 4.02  | 8000 | 0.2847          | 0.2176 |
| 0.1011        | 4.27  | 8500 | 0.2763          | 0.2124 |
| 0.0981        | 4.52  | 9000 | 0.2754          | 0.2115 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2