File size: 3,997 Bytes
68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 89b25b3 68d0f29 4f39b79 68d0f29 4f39b79 68d0f29 c057e1c 68d0f29 4f39b79 68d0f29 c057e1c 68d0f29 c057e1c 68d0f29 c057e1c 68d0f29 c057e1c 68d0f29 4f39b79 68d0f29 |
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 |
---
language:
- tr
license: apache-2.0
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
datasets:
- common_voice
metrics:
- wer
base_model: facebook/wav2vec2-large-xlsr-53
model-index:
- name: Ozcan Gundes XLSR Wav2Vec2 Large Turkish
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- type: wer
value: 29.62
name: Test WER
---
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish 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
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish")
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 Turkish 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", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("ozcangundes/wav2vec2-large-xlsr-53-turkish")
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):
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)
# 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**: 29.62 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/drive/1hesw9z_kFFINT93jBvGuFspOLrHx10AE?usp=sharing) |