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---
license: apache-2.0
language:
- bn
base_model:
- openai/whisper-small
pipeline_tag: automatic-speech-recognition
---
BengaliRegionalASR trained on bengali regional dialact dataset.
# Try the model
```py
import os
import librosa
import torch, torchaudio
import numpy as np
from transformers import WhisperTokenizer ,WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration
model_path_ = "sha1779/BengaliRegionalASR"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_path_)
tokenizer = WhisperTokenizer.from_pretrained(model_path_)
processor = WhisperProcessor.from_pretrained(model_path_)
model = WhisperForConditionalGeneration.from_pretrained(model_path_).to(device)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="bengali", task="transcribe")
mp3_path = "https://huggingface.co/sha1779/BengaliRegionalASR/resolve/main/Mp3/common_voice_bn_31617644.mp3"
speech_array, sampling_rate = librosa.load(mp3_path, sr=16000)
speech_array = librosa.resample(np.asarray(speech_array), orig_sr=sampling_rate, target_sr=16000)
input_features = feature_extractor(speech_array, sampling_rate=16000, return_tensors="pt").input_features
predicted_ids = model.generate(inputs=input_features.to(device))[0]
transcription = processor.decode(predicted_ids, skip_special_tokens=True)
print(transcription)
```
# Evaluation
Word Error Rate 0.65 %
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