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Model: arbml/wav2vec2-large-xlsr-53-arabic-egyptian
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---
language: ???
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Arabic Egyptian by Zaid
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ???
type: common_voice
args: ???
metrics:
- name: Test WER
type: wer
value: ???
---
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil 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", "???", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("Zaid/wav2vec2-large-xlsr-53-arabic-egyptian")
model = Wav2Vec2ForCTC.from_pretrained("Zaid/wav2vec2-large-xlsr-53-arabic-egyptian")
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):
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["speech"][:2], 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["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} 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", "???", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Zaid/wav2vec2-large-xlsr-53-arabic-egyptian")
model = Wav2Vec2ForCTC.from_pretrained("Zaid/wav2vec2-large-xlsr-53-arabic-egyptian")
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**: ??? %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found ???

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{
"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
"activation_dropout": 0.0,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForCTC"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"conv_bias": true,
"conv_dim": [
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512,
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512,
512,
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512
],
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3,
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3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
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2
],
"ctc_loss_reduction": "mean",
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"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.0,
"final_dropout": 0.0,
"gradient_checkpointing": true,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_channel_length": 10,
"mask_channel_min_space": 1,
"mask_channel_other": 0.0,
"mask_channel_prob": 0.0,
"mask_channel_selection": "static",
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"mask_time_length": 10,
"mask_time_min_space": 1,
"mask_time_other": 0.0,
"mask_time_prob": 0.05,
"mask_time_selection": "static",
"model_type": "wav2vec2",
"num_attention_heads": 16,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 24,
"pad_token_id": 44,
"transformers_version": "4.4.0",
"vocab_size": 45
}

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"""Arabic Speech Corpus"""
from __future__ import absolute_import, division, print_function
import os
import datasets
_CITATION = """
"""
_DESCRIPTION = """\
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
```
"""
_URL = "mgb3.zip"
corrupt_files = ['familyKids_02_first_12min.wav','sports_04_first_12min.wav',
'cooking_05_first_12min.wav', 'moviesDrama_07_first_12min.wav','science_06_first_12min.wav',
'comedy_09_first_12min.wav','cultural_08_first_12min.wav','familyKids_11_first_12min.wav',
'science_10_first_12min.wav']
import soundfile as sf
class EgyptianSpeechCorpusConfig(datasets.BuilderConfig):
"""BuilderConfig for EgyptianSpeechCorpus."""
def __init__(self, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
super(EgyptianSpeechCorpusConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
def map_to_array(batch):
start, stop = batch['segment'].split('_')
speech_array, _ = sf.read(batch["file"], start = start, stop = stop)
batch["speech"] = speech_array
return batch
class EgyptionSpeechCorpus(datasets.GeneratorBasedBuilder):
"""EgyptianSpeechCorpus dataset."""
BUILDER_CONFIGS = [
EgyptianSpeechCorpusConfig(name="clean", description="'Clean' speech."),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"text": datasets.Value("string"),
"segment": datasets.Value("string")
}
),
supervised_keys=("file", "text"),
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
self.archive_path = '/content/mgb3'
return [
datasets.SplitGenerator(name="train", gen_kwargs={"archive_path": os.path.join(self.archive_path, "adapt")}),
datasets.SplitGenerator(name="dev", gen_kwargs={"archive_path": os.path.join(self.archive_path, "dev")}),
datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": os.path.join(self.archive_path, "test")}),
]
def _generate_examples(self, archive_path):
"""Generate examples from a Librispeech archive_path."""
text_dir = os.path.join(archive_path, "Alaa")
wav_dir = os.path.join(self.archive_path, "wav")
segments_file = os.path.join(text_dir, "text_noverlap")
with open(segments_file, "r", encoding="utf-8") as f:
for _id, line in enumerate(f):
segment = line.split(' ')[0]
text = ' '.join(line.split(' ')[1:])
wav_file = '_'.join(segment.split('_')[:4]) +'.wav'
start, stop = segment.split('_')[4:6]
wav_path = os.path.join(wav_dir, wav_file)
if (wav_file in corrupt_files) or (wav_file not in os.listdir(wav_dir)):
continue
example = {
"file": wav_path,
"text": text,
"segment":('_').join([start, stop])
}
yield str(_id), example

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