Update README.md

README.md

@@ -21,10 +21,13 @@ This model is trained on this barishal regional data only. The dataset is taken
 
 ```py
 import os
+import requests
 import librosa
-import torch, torchaudio
+import torch
 import numpy as np
-from transformers import WhisperTokenizer ,WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration
+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_)
@@ -33,28 +36,53 @@ 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
+# 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
 
-predicted_ids = model.generate(inputs=input_features.to(device))[0]
+    # Generate transcription
+    print("Generating transcription...")
+    predicted_ids = model.generate(inputs=input_features.to(device))[0]
+    transcription = processor.decode(predicted_ids, skip_special_tokens=True)
 
-transcription = processor.decode(predicted_ids, skip_special_tokens=True)
+    # Print the transcription
+    print("Transcription:", transcription)
 
-print(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, torchaudio
+import torch
 import numpy as np
-from transformers import WhisperTokenizer ,WhisperProcessor, WhisperFeatureExtractor, WhisperForConditionalGeneration
+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_)
@@ -63,35 +91,50 @@ 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"
+# 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}")
 
-speech_array, sampling_rate = librosa.load(mp3_path, sr=16000)
+# Load audio
+speech_array, sampling_rate = librosa.load(local_audio_path, sr=16000)
 
-# Split audio into 30-second chunks with 5-second overlap
+# Define chunk parameters
 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)
+# Split audio into chunks
+chunks = [
+    speech_array[start : start + chunk_size]
+    for start in range(0, len(speech_array), chunk_size - overlap_size)
+]
 
-# Process each chunk
+# 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)
-    print(transcription,end=" ")
+    transcriptions.append(transcription)
+
+# Combine and print the transcriptions
+print(" ".join(transcriptions))
+
+# Clean up temporary file
+os.remove(local_audio_path)
 
 ```