language: el datasets:

• common_voice tags:
• audio
• hf-asr-leaderboard
• automatic-speech-recognition
• speech
• xlsr-fine-tuning-week license: apache-2.0 model-index:
• name: XLSR Wav2Vec2 Greek by Lighteternal results:
  • task: name: Speech Recognition type: automatic-speech-recognition dataset: name: CommonVoice (EL), CSS10 (EL) type: CCS10 + mozilla-foundation/common_voice_7_0 args: el metrics:
    • name: Test WER type: wer value: 10.497628
    • name: Test CER type: cer value: 2.875260

Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model

By the Hellenic Army Academy and the Technical University of Crete

• language: el
• licence: apache-2.0
• dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets + CSS10 (EL), 1.22GB: https://github.com/Kyubyong/css10
• model: XLSR-Wav2Vec2, trained for 50 epochs
• metrics: Word Error Rate (WER)

Model description

UPDATE: We repeated the fine-tuning process using an additional 1.22GB dataset from CSS10.

Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, Facebook AI presented XLSR-Wav2Vec2. XLSR stands for cross-lingual speech representations and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.

Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.

This model was trained for 50 epochs on a single NVIDIA RTX 3080, for aprox. 8hrs.

How to use for inference:

For live demo, make sure that speech files are sampled at 16kHz.

Instructions to test on CommonVoice extracts are provided in the ASR_Inference.ipynb. Snippet also available below:

#!/usr/bin/env python
# coding: utf-8

# Loading dependencies and defining preprocessing functions 

from transformers import Wav2Vec2ForCTC
from transformers import Wav2Vec2Processor
from datasets import load_dataset, load_metric
import re
import torchaudio
import librosa
import numpy as np
from datasets import load_dataset, load_metric
import torch

chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'

def remove_special_characters(batch):
    batch["text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
    return batch

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = speech_array[0].numpy()
    batch["sampling_rate"] = sampling_rate
    batch["target_text"] = batch["text"]
    return batch

def resample(batch):
    batch["speech"] = librosa.resample(np.asarray(batch["speech"]), 48_000, 16_000)
    batch["sampling_rate"] = 16_000
    return batch

def prepare_dataset(batch):
    # check that all files have the correct sampling rate
    assert (
        len(set(batch["sampling_rate"])) == 1
    ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

    batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values
    
    with processor.as_target_processor():
        batch["labels"] = processor(batch["target_text"]).input_ids
    return batch


# Loading model and dataset processor

model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")


# Preparing speech dataset to be suitable for inference

common_voice_test = load_dataset("common_voice", "el", split="test")

common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])

common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=["sentence"])

common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)

common_voice_test = common_voice_test.map(resample, num_proc=8)

common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)


# Loading test dataset

common_voice_test_transcription = load_dataset("common_voice", "el", split="test")


#Performing inference on a random sample. Change the "example" value to try inference on different CommonVoice extracts

example = 123

input_dict = processor(common_voice_test["input_values"][example], return_tensors="pt", sampling_rate=16_000, padding=True)

logits = model(input_dict.input_values.to("cuda")).logits

pred_ids = torch.argmax(logits, dim=-1)

print("Prediction:")
print(processor.decode(pred_ids[0]))
# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς

print("\\\\
Reference:")
print(common_voice_test_transcription["sentence"][example].lower())
# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.

Evaluation

The model can be evaluated as follows on the Greek test data of Common Voice.

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "el", split="test") 
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek") 
model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
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: 10.497628 %

How to use for training:

Instructions and code to replicate the process are provided in the Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers.ipynb notebook.

Metrics

Acknowledgement

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)

Based on the tutorial of Patrick von Platen: https://huggingface.co/blog/fine-tune-xlsr-wav2vec2 Original colab notebook here: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=V7YOT2mnUiea