File size: 4,054 Bytes
466bfc4 97fd903 466bfc4 97fd903 466bfc4 2964fff 095796f 466bfc4 2964fff 466bfc4 2964fff 095796f 2964fff 75c0e4f 2964fff 095796f 466bfc4 4b3a978 466bfc4 97fd903 466bfc4 97fd903 466bfc4 2964fff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
---
language:
- mt
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Maltese
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 8
args: mt
metrics:
- type: wer
value: 15.967
name: Test WER
- name: Test CER
type: cer
value: 3.657
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-300M - Maltese
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1895
- Wer: 0.1984
## 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: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4219 | 3.6 | 400 | 3.3127 | 1.0 |
| 3.0399 | 7.21 | 800 | 3.0330 | 1.0 |
| 1.5756 | 10.81 | 1200 | 0.6108 | 0.5724 |
| 1.0995 | 14.41 | 1600 | 0.3091 | 0.3154 |
| 0.9639 | 18.02 | 2000 | 0.2596 | 0.2841 |
| 0.9032 | 21.62 | 2400 | 0.2270 | 0.2514 |
| 0.8145 | 25.23 | 2800 | 0.2172 | 0.2483 |
| 0.7845 | 28.83 | 3200 | 0.2084 | 0.2333 |
| 0.7694 | 32.43 | 3600 | 0.1974 | 0.2234 |
| 0.7333 | 36.04 | 4000 | 0.2020 | 0.2185 |
| 0.693 | 39.64 | 4400 | 0.1947 | 0.2148 |
| 0.6802 | 43.24 | 4800 | 0.1960 | 0.2102 |
| 0.667 | 46.85 | 5200 | 0.1904 | 0.2072 |
| 0.6486 | 50.45 | 5600 | 0.1881 | 0.2009 |
| 0.6339 | 54.05 | 6000 | 0.1877 | 0.1989 |
| 0.6254 | 57.66 | 6400 | 0.1893 | 0.2003 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --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-xls-r-300m-mt-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", 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
# => "għadu jilagħbu ċirku tant bilfondi"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 19.853 | 15.967 | |