GetmanY1/wav2vec2-base-fi-150k-finetuned

Finnish Wav2vec2-Base ASR

GetmanY1/wav2vec2-base-fi-150k fine-tuned on 4600 hours of Finnish speech on 16kHz sampled speech audio:

• 1500 hours of Lahjoita puhetta (Donate Speech) (colloquial Finnish)
• 3100 hours of the Finnish Parliament dataset

When using the model make sure that your speech input is also sampled at 16Khz.

Model description

The Finnish Wav2Vec2 Base has the same architecture and uses the same training objective as the English and multilingual one described in Paper.

GetmanY1/wav2vec2-base-fi-150k is a large-scale, 95-million parameter monolingual model pre-trained on 158k hours of unlabeled Finnish speech, including KAVI radio and television archive materials, Lahjoita puhetta (Donate Speech), Finnish Parliament, Finnish VoxPopuli.

You can read more about the pre-trained model from this paper. The training scripts are available on GitHub.

Intended uses

You can use this model for Finnish ASR (speech-to-text).

How to use

To transcribe audio files the model can be used as a standalone acoustic model as follows:

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch

# load model and processor
processor = Wav2Vec2Processor.from_pretrained("GetmanY1/wav2vec2-base-fi-150k-finetuned")
model = Wav2Vec2ForCTC.from_pretrained("GetmanY1/wav2vec2-base-fi-150k-finetuned")

# load dummy dataset and read soundfiles
ds = load_dataset("mozilla-foundation/common_voice_16_1", "fi", split='test')

# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1

# retrieve logits
logits = model(input_values).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)

Team Members

• Yaroslav Getman, Hugging Face profile, LinkedIn profile
• Tamas Grosz, Hugging Face profile, LinkedIn profile

Feel free to contact us for more details 🤗