README.md

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language: el #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of this site. datasets: - common_voice #TODO: remove if you did not use the common voice dataset - TODO: add more datasets if you have used additional datasets. Make sure to use the exact same dataset name as the one found here. If the dataset can not be found in the official datasets, just give it a new name metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Greek XLSR Wav2Vec2 Large 53 - CV + CSS10 #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like Elgeish XLSR Wav2Vec2 Large 53 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice el #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of this site. type: common_voice args: el #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of this site. metrics: - name: Test WER type: wer value: 34.75 #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). Please remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value

Wav2Vec2-Large-XLSR-53-greek #TODO: replace language with your {language}, e.g. French

Fine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 datasets. #TODO: replace {language} with your language, e.g. French and eventually add more datasets that were used and eventually remove common voice if model was not trained on 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:

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

test_dataset = load_dataset("common_voice", "el", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.

processor = Wav2Vec2Processor.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`

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 greek test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, e.g. French

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") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\'\�]'  # TODO: adapt this list to include all special characters you removed from the data
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: 34.75 % # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.

Training

The Common Voice train, validation, and CSS10 datasets were used for training, added as extra split to the dataset. The sampling rate and format of the CSS10 files is different, hence the function speech_file_to_array_fn was changed to: # TODO: adapt to state all the datasets that were used for training.

    def speech_file_to_array_fn(batch):
        try:
            speech_array, sampling_rate = sf.read(batch["path"] + ".wav")
        except:
            speech_array, sampling_rate = librosa.load(batch["path"], sr = 16000, res_type='zero_order_hold')
            sf.write(batch["path"] + ".wav", speech_array, sampling_rate, subtype='PCM_24')
        batch["speech"] = speech_array
        batch["sampling_rate"] = sampling_rate
        batch["target_text"] = batch["text"]
        return batch

As suggested by Florian Zimmermeister.

The script used for training can be found in run_common_voice.py, still pending of PR. The only changes are to speech_file_to_array_fn. Batch size was kept at 32 (using gradient_accumulation_steps) using one of the OVH machines, with a V100 GPU (thank you very much OVH). The model trained for 40 epochs, the first 20 with the train+validation splits, and then extra split was added with the data from CSS10 at the 20th epoch. # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.

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