Wav2vec2 Large Xlsr 53 Finnish

🚀 Wav2Vec2-Large-XLSR-53-Finnish

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Finnish, aiming to achieve high - quality automatic speech recognition.

✨ Features

• Datasets: Trained on multiple Finnish datasets including Common Voice, CSS10 and Finnish parliament session 2.
• Metrics: Evaluated using Word Error Rate (WER).
• Tags: Related to audio, automatic - speech - recognition, speech and xlsr - fine - tuning - week.
• License: Licensed under the Apache 2.0 license.

🚀 Quick Start

When using this model, make sure that your speech input is sampled at 16kHz.

💻 Usage Examples

Basic Usage

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

test_dataset = load_dataset("common_voice", "fi", split="test[:2%]")

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

resampler = lambda sr, y: librosa.resample(y.squeeze(), sr, 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(sampling_rate, speech_array.numpy()).squeeze()
	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])

📚 Documentation

Evaluation

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

import librosa
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")
wer = load_metric("wer")

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

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\"\%\'\"\�\'\...\…\–\é]'

resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)

# Preprocessing the datasets.
# We need to read the audio 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()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the audio 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: 35.43 %

Training

The Common Voice train, validation, and other datasets were used for training as well as CSS10 and Finnish parliament session 2.

The script used for training can be found here # 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.

📄 License

This model is licensed under the Apache 2.0 license.