Bangla Wav2Vec2 — 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 tagfull_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.jsonlistsarchitectures: ["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 standardWav2Vec2ForCTC/AutoModelForCTCclasses fromtransformers(see usage below).
Usage
import torch
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model_id = "SayedShaun/bangla-wave2vec2"
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.