|
--- |
|
language: fa |
|
datasets: |
|
- common_voice |
|
metrics: |
|
- wer |
|
- cer |
|
tags: |
|
- audio |
|
- automatic-speech-recognition |
|
- speech |
|
- xlsr-fine-tuning-week |
|
license: apache-2.0 |
|
model-index: |
|
- name: XLSR Wav2Vec2 Persian by Jonatas Grosman |
|
results: |
|
- task: |
|
name: Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice fa |
|
type: common_voice |
|
args: fa |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 30.12 |
|
- name: Test CER |
|
type: cer |
|
value: 7.37 |
|
--- |
|
|
|
# Fine-tuned XLSR-53 large model for speech recognition in Persian |
|
|
|
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Persian using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). |
|
When using this model, make sure that your speech input is sampled at 16kHz. |
|
|
|
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) |
|
|
|
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint |
|
|
|
## Usage |
|
|
|
The model can be used directly (without a language model) as follows... |
|
|
|
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: |
|
|
|
```python |
|
from huggingsound import SpeechRecognitionModel |
|
|
|
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-persian") |
|
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] |
|
|
|
transcriptions = model.transcribe(audio_paths) |
|
``` |
|
|
|
Writing your own inference script: |
|
|
|
```python |
|
import torch |
|
import librosa |
|
from datasets import load_dataset |
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
|
|
|
LANG_ID = "fa" |
|
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-persian" |
|
SAMPLES = 5 |
|
|
|
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") |
|
|
|
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
|
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
|
|
|
# Preprocessing the datasets. |
|
# We need to read the audio files as arrays |
|
def speech_file_to_array_fn(batch): |
|
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
|
batch["speech"] = speech_array |
|
batch["sentence"] = batch["sentence"].upper() |
|
return batch |
|
|
|
test_dataset = test_dataset.map(speech_file_to_array_fn) |
|
inputs = processor(test_dataset["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) |
|
predicted_sentences = processor.batch_decode(predicted_ids) |
|
|
|
for i, predicted_sentence in enumerate(predicted_sentences): |
|
print("-" * 100) |
|
print("Reference:", test_dataset[i]["sentence"]) |
|
print("Prediction:", predicted_sentence) |
|
``` |
|
|
|
| Reference | Prediction | |
|
| ------------- | ------------- | |
|
| از مهمونداری کنار بکشم | از مهمانداری کنار بکشم | |
|
| برو از مهرداد بپرس. | برو از ماقدعاد به پرس | |
|
| خب ، تو چیكار می كنی؟ | خوب تو چیکار می کنی | |
|
| مسقط پایتخت عمان در عربی به معنای محل سقوط است | مسقط پایتخت عمان در عربی به بعنای محل سقوط است | |
|
| آه، نه اصلاُ! | اهنه اصلا | |
|
| توانست | توانست | |
|
| قصیده فن شعر میگوید ای دوستان | قصیده فن شعر میگوید ایدوستون | |
|
| دو استایل متفاوت دارین | دوبوست داریل و متفاوت بری | |
|
| دو روز قبل از کریسمس ؟ | اون مفتود پش پشش | |
|
| ساعت های کاری چیست؟ | این توری که موشیکل خب | |
|
|
|
## Evaluation |
|
|
|
The model can be evaluated as follows on the Persian test data of Common Voice. |
|
|
|
```python |
|
import torch |
|
import re |
|
import librosa |
|
from datasets import load_dataset, load_metric |
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
|
|
|
LANG_ID = "fa" |
|
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-persian" |
|
DEVICE = "cuda" |
|
|
|
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", |
|
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", |
|
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", |
|
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", |
|
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"] |
|
|
|
test_dataset = load_dataset("common_voice", LANG_ID, split="test") |
|
|
|
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py |
|
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py |
|
|
|
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" |
|
|
|
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
|
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
|
model.to(DEVICE) |
|
|
|
# Preprocessing the datasets. |
|
# We need to read the audio files as arrays |
|
def speech_file_to_array_fn(batch): |
|
with warnings.catch_warnings(): |
|
warnings.simplefilter("ignore") |
|
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
|
batch["speech"] = speech_array |
|
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() |
|
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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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) |
|
|
|
predictions = [x.upper() for x in result["pred_strings"]] |
|
references = [x.upper() for x in result["sentence"]] |
|
|
|
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
|
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
|
``` |
|
|
|
**Test Result**: |
|
|
|
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-22). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. |
|
|
|
| Model | WER | CER | |
|
| ------------- | ------------- | ------------- | |
|
| jonatasgrosman/wav2vec2-large-xlsr-53-persian | **30.12%** | **7.37%** | |
|
| m3hrdadfi/wav2vec2-large-xlsr-persian-v2 | 33.85% | 8.79% | |
|
| m3hrdadfi/wav2vec2-large-xlsr-persian | 34.37% | 8.98% | |
|
|
|
## Citation |
|
If you want to cite this model you can use this: |
|
|
|
```bibtex |
|
@misc{grosman2021xlsr53-large-persian, |
|
title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}ersian}, |
|
author={Grosman, Jonatas}, |
|
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-persian}}, |
|
year={2021} |
|
} |
|
``` |
