Bangla Wav2Vec2 (Unigram) — ASR Model

A Wav2Vec2-based CTC acoustic model for Bengali (Bangla) automatic speech recognition (ASR).

Attribution

This model's weights and configuration were not trained by the uploader. They are mirrored from the Kaggle dataset wv-shru-v3-s6 published by Kaggle user qdv206. All credit for training and releasing the original model goes to the original author. This upload exists to make the checkpoint easier to load and use via the huggingface_hub / transformers ecosystem.

If you use this model, please credit the original author and link back to the source dataset above.

Model details

Architecture: Wav2Vec2 (CTC head), hidden_size=1024, checkpoint tag full_shru_v2_s20
Task: Automatic Speech Recognition (speech-to-text), Bengali script output
Vocabulary: Character-level CTC vocabulary (90 tokens) covering Bengali script, digits, and basic punctuation
Sampling rate: 16 kHz mono audio expected
Tokenizer/Processor: Wav2Vec2CTCTokenizer + Wav2Vec2FeatureExtractor (Wav2Vec2Processor)

Note: the original config.json lists architectures: ["Wav2Vec2ForCTCV2"], a custom class name used in the original training pipeline. The underlying weights are a standard Wav2Vec2-for-CTC architecture, so the model loads with the standard Wav2Vec2ForCTC / AutoModelForCTC classes from transformers (see usage below).

Usage

import torch
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

model_id = "SayedShaun/bangla-wave2vec2-unigram"

processor = Wav2Vec2Processor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

speech, sr = sf.read("audio.wav")  # expects 16kHz mono
inputs = processor(speech, sampling_rate=16000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
print(transcription)

Source

Original dataset/model: https://www.kaggle.com/datasets/qdv206/wv-shru-v3-s6/data
Mirrored to Hugging Face for easier programmatic access.

License

No explicit license was specified by the original author on Kaggle. Please refer to the original dataset page for usage terms, and contact the original author for clarification if you plan to use this commercially.

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