update

README.md

@@ -5,4 +5,34 @@ language:
 base_model:
 - openai/whisper-small
 pipeline_tag: automatic-speech-recognition
----
+
+---
+```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 = "/kaggle/input/barishal-data/valid_barishal (104).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)
+
+
+```