
# Wav2Vec2-Large-XLSR-53-finnish

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on finnish using the [Common Voice](https://huggingface.co/datasets/common_voice)  and [CSS10 finnish: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset).
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[: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-finnish") #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-finnish") #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 finnish 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", "fi", 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("vasilis/wav2vec2-large-xlsr-53-finnish")
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-finnish")
model.to("cuda")

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

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()
    for key, value in replacements.items():
        batch["sentence"] = batch["sentence"].replace(key, value)
    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**:  38.335242 %


## Training


The Common Voice train dataset was used for training. Also all of `CSS10 Finnish` was used using the normalized transcripts.
After 20000 steps the models was finetuned using the common voice train and validation sets for 2000 steps more.


{"language": "fi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"CSS10 finnish": "Single Speaker Speech Dataset"}], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - finnish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fi", "type": "common_voice", "args": "fi"}, "metrics": [{"type": "wer", "value": 38.335242, "name": "Test WER"}, {"type": "cer", "value": 6.552408, "name": "Test CER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week fi license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice  and CSS10 finnish: Single Speaker Speech Dataset.
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:




## Evaluation

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




Test Result:  38.335242 %


## Training


The Common Voice train dataset was used for training. Also all of 'CSS10 Finnish' was used using the normalized transcripts.
After 20000 steps the models was finetuned using the common voice train and validation sets for 2000 steps more.


# Wav2Vec2-Large-XLSR-53-finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice  and CSS10 finnish: Single Speaker Speech Dataset.
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: ## Evaluation

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




Test Result:  38.335242 % ## Training


The Common Voice train dataset was used for training. Also all of 'CSS10 Finnish' was used using the normalized transcripts.
After 20000 steps the models was finetuned using the common voice train and validation sets for 2000 steps more.
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice  and CSS10 finnish: Single Speaker Speech Dataset.
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: ## Evaluation

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




Test Result:  38.335242 % ## Training


The Common Voice train dataset was used for training. Also all of 'CSS10 Finnish' was used using the normalized transcripts.
After 20000 steps the models was finetuned using the common voice train and validation sets for 2000 steps more.
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice  and CSS10 finnish: Single Speaker Speech Dataset.
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:## Evaluation

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




Test Result:  38.335242 %## Training


The Common Voice train dataset was used for training. Also all of 'CSS10 Finnish' was used using the normalized transcripts.
After 20000 steps the models was finetuned using the common voice train and validation sets for 2000 steps more.