updates model

d73c028 about 3 years ago

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	---
	language: sv-SE
	datasets:
	- common_voice
	- NST Swedish ASR Database
	metrics:
	- wer
	- cer
	tags:
	- audio
	- automatic-speech-recognition
	- speech
	- xlsr-fine-tuning-week
	license: apache-2.0
	model-index:
	- name: V XLSR Wav2Vec2 Large 53 - Swedish
	results:
	- task:
	name: Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice sv-SE
	type: common_voice
	args: sv-SE
	metrics:
	- name: Test WER
	type: wer
	value: 14.695793
	- name: Test CER
	type: cer
	value: 5.264666
	---

	# Wav2Vec2-Large-XLSR-53-Swedish

	Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice) and parts for the [NST Swedish ASR Database](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-16/).
	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", "sv-SE", 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("vasilis/wav2vec2-large-xlsr-53-swedish") #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("vasilis/wav2vec2-large-xlsr-53-swedish") #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 Swedish 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", "sv-SE", split="test")
	wer = load_metric("wer")

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

	chars_to_ignore_regex = "[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']" # TODO: adapt this list to include all special characters you removed from the data

	resampler = {
	48_000: torchaudio.transforms.Resample(48_000, 16_000),
	44100: torchaudio.transforms.Resample(44100, 16_000),
	32000: torchaudio.transforms.Resample(32000, 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[sampling_rate](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"])))
	print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))

	```

	Test Result: 14.695793 %

	## Training

	As first step used Common Voice train dataset and parts from NST
	as can be found [here](https://github.com/se-asr/nst/tree/master).
	Part of NST where removed using this mask

	```python
	mask = [(5 < len(x.split()) < 20) and np.average([len(entry) for entry in x.split()]) > 5 for x in dataset['transcript'].tolist()]
	```

	After training like this for 20000 steps the model was finetuned on all of nst data using the mask

	```python
	mask = [(1 < len(x.split()) < 25) and np.average([len(entry) for entry in x.split()]) > 3 for x in dataset['transcript'].tolist()]
	```

	and all of common voice for 100000 more steps approximately 16 epochs.