---
language: su
datasets:
- openslr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Sundanese by cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR High quality TTS data for Sundanese
type: OpenSLR
args: su
metrics:
- name: Test WER
type: wer
value: 6.19
---
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/).
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, load_metric, Dataset
from datasets.utils.download_manager import DownloadManager
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb).
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
import re
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
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 audio 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**: 6.19 %
## Training
[OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)