---
license: apache-2.0
language:
- bn
base_model:
- openai/whisper-small

pipeline_tag: automatic-speech-recognition

---
BengaliRegionalASR trained on bengali regional dialact dataset. [sha1779/Bengali_Regional_dataset](https://huggingface.co/datasets/sha1779/Bengali_Regional_dataset)


This model is trained on this barishal regional data only. The dataset is taken from [ভাষা-বিচিত্রা: ASR for Regional Dialects](https://www.kaggle.com/competitions/ben10) competition.

# Try the model

```bash
!pip install librosa torch torchaudio transformers
```

```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/valid_barishal%20(1).wav"

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)

```

## For larger audio , more than 30s
```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/valid_barishal%20(1).wav"


speech_array, sampling_rate = librosa.load(mp3_path, sr=16000)

# Split audio into 30-second chunks with 5-second overlap
chunk_duration = 30  # seconds
overlap = 5  # seconds
chunk_size = int(chunk_duration * sampling_rate)
overlap_size = int(overlap * sampling_rate)

chunks = []
for start in range(0, len(speech_array), chunk_size - overlap_size):
    end = start + chunk_size
    chunk = speech_array[start:end]
    chunks.append(chunk)

# Process each chunk
transcriptions = []
for i, chunk in enumerate(chunks):
     
    # Resample and extract features
    chunk = librosa.resample(np.asarray(chunk), orig_sr=sampling_rate, target_sr=16000)
    input_features = feature_extractor(chunk, sampling_rate=16000, return_tensors="pt").input_features
    
    # Generate transcription
    predicted_ids = model.generate(inputs=input_features.to(device))[0]
    transcription = processor.decode(predicted_ids, skip_special_tokens=True)
    print(transcription,end=" ")

``` 

# Evaluation
Word Error Rate 0.65 %