Are you ready to dive into the world of automatic speech recognition (ASR) using the powerful Wav2Vec2 model? This blog post will guide you through using the Wav2Vec2 Large 100k Voxpopuli fine-tuned with Common Voice and M-AILABS in the Russian language. We will walk through the setup process, code implementation, and some troubleshooting tips. Let’s get started!

Getting Started

Before we begin coding, ensure you have the required libraries installed: transformers and torchaudio. You can install them using pip:

pip install transformers torchaudio

Loading the Model

Now let’s load the model for our speech recognition task. This model has been trained using multiple datasets, which enhances its performance in recognizing Russian language audio.

from transformers import AutoTokenizer, Wav2Vec2ForCTC

tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-russian")

Preparing Your Dataset

Once your model is ready, you will need to prepare your dataset. Here’s how to load and preprocess the Common Voice dataset:

from datasets import load_dataset

dataset = load_dataset("common_voice", "ru", split="test", data_dir=".cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)

def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    batch["sentence"] = re.sub(r'[^0-9a-zA-Zа-яА-ЯёЁ\s]', '', batch["sentence"]).lower().replace('’', '')
    return batch

ds = dataset.map(map_to_array)

Making Predictions

After preprocessing your dataset, you can now utilize the model for making predictions:

result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))

Understanding the Code with an Analogy

Think of the Wav2Vec2 speech recognition process like building a recipe in a kitchen. Each ingredient represents a piece of code you write. Just like gathering your ingredients (loading the tokenizer and model), preparing your ingredients (loading and resampling the audio dataset), and finally cooking (making predictions with the model) leads to a delicious dish, every step in the code contributes to accurate speech recognition results. If you miss one ingredient, the entire dish may not turn out as expected, just like how incomplete data can affect accuracy.

Troubleshooting

If you encounter any issues, here are some troubleshooting tips:

• Error while loading the model: Ensure the model name is spelled correctly and that you are connected to the internet.
• Issues with dataset loading: Confirm that the directory path to the dataset is correct and that the dataset is properly downloaded.
• Wrong input format: Check if the audio files meet the required specifications (format and sampling rate).

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Conclusion

In this blog, you learned how to set up and run automatic speech recognition using the Wav2Vec2 fine-tuned model for Russian. By following the steps outlined above, you can now work with audio datasets and obtain meaningful predictions.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.