README.md

---
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 requests
import librosa
import torch
import numpy as np
from transformers import WhisperTokenizer, WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration

# Define model and device
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 URL
mp3_url = "https://huggingface.co/sha1779/BengaliRegionalASR/resolve/main/Mp3/valid_barishal%20(1).wav"
local_audio_path = "temp_audio.wav"

# Download the MP3 file
print("Downloading audio file...")
response = requests.get(mp3_url)
if response.status_code == 200:
    with open(local_audio_path, 'wb') as f:
        f.write(response.content)
    print("Download complete.")
else:
    raise Exception(f"Failed to download file. HTTP status code: {response.status_code}")

# Load and preprocess the audio
try:
    print("Processing audio file...")
    speech_array, sampling_rate = librosa.load(local_audio_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

    # Generate transcription
    print("Generating transcription...")
    predicted_ids = model.generate(inputs=input_features.to(device))[0]
    transcription = processor.decode(predicted_ids, skip_special_tokens=True)

    # Print the transcription
    print("Transcription:", transcription)

finally:
    # Clean up: delete the temporary audio file
    if os.path.exists(local_audio_path):
        os.remove(local_audio_path)
        print("Temporary audio file deleted.")

```

## For larger audio , more than 30s
```py
import os
import requests
import librosa
import torch
import numpy as np
from transformers import WhisperTokenizer, WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration

# Define model and device
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")

# Remote MP3 file URL
mp3_url = "https://huggingface.co/sha1779/BengaliRegionalASR/resolve/main/Mp3/valid_barishal%20(1).wav"
local_audio_path = "temp_audio.wav"

# Download the MP3 file
response = requests.get(mp3_url)
if response.status_code == 200:
    with open(local_audio_path, 'wb') as f:
        f.write(response.content)
else:
    raise Exception(f"Failed to download file. HTTP status code: {response.status_code}")

# Load audio
speech_array, sampling_rate = librosa.load(local_audio_path, sr=16000)

# Define chunk parameters
chunk_duration = 30  # seconds
overlap = 5  # seconds
chunk_size = int(chunk_duration * sampling_rate)
overlap_size = int(overlap * sampling_rate)

# Split audio into chunks
chunks = [
    speech_array[start : start + chunk_size]
    for start in range(0, len(speech_array), chunk_size - overlap_size)
]

# Process and transcribe 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)
    transcriptions.append(transcription)

# Combine and print the transcriptions
print(" ".join(transcriptions))

# Clean up temporary file
os.remove(local_audio_path)

``` 

# Evaluation
Word Error Rate 0.65 %