wav2vec2-large-xlsr-53-greek / 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](https://huggingface.co/languages) site.
	datasets:
	- common_voice #TODO: remove if you did not use the common voice dataset
	- CSS10
	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](https://huggingface.co/languages) 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](https://huggingface.co/languages) site.
	metrics:
	- name: Test WER
	type: wer
	value: 20.89 #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

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10) datasets.
	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", "el", split="test")

	processor = Wav2Vec2Processor.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek")
	model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek")

	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.

	```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", "el", split="test")
	wer = load_metric("wer")

	processor = Wav2Vec2Processor.from_pretrained("PereLluis13/wav2vec2-large-xlsr-53-greek")
	model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/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: 20.89 %

	## 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:
	```
	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](https://github.com/flozi00).

	The script used for training can be found in [run_common_voice.py](examples/research_projects/wav2vec2/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](https://www.ovh.com/) machines, with a V100 GPU (thank you very much [OVH](https://www.ovh.com/)). 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.