Delete audio.py

audio.py

@@ -1,58 +0,0 @@
-import librosa
-import numpy as np
-
-def extract_features(audio_path):
-    y, sr = librosa.load(audio_path, sr=16000)
-    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
-    return np.mean(mfccs.T, axis=0)
-
-# Example usage
-features = extract_features("path/to/audio/file.wav")
-
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer, MarianMTModel, MarianTokenizer
-
-# Load pre-trained models
-speech_recognition_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
-speech_recognition_tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
-translation_model = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-hi")
-translation_tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-hi")
-
-from transformers import pipeline
-
-# Example inference pipeline
-def translate_audio(audio_path):
-    # Speech Recognition
-    speech_input = speech_recognition_tokenizer(extract_features(audio_path), return_tensors="pt").input_values
-    logits = speech_recognition_model(speech_input).logits
-    transcription = speech_recognition_tokenizer.batch_decode(torch.argmax(logits, dim=-1))[0]
-    
-    # Translation
-    translated = translation_model.generate(**translation_tokenizer.prepare_seq2seq_batch(transcription, return_tensors="pt"))
-    translation = translation_tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
-    
-    return translation
-
-# Save the models and tokenizer
-speech_recognition_model.save_pretrained("path/to/save/wav2vec2")
-speech_recognition_tokenizer.save_pretrained("path/to/save/wav2vec2")
-translation_model.save_pretrained("path/to/save/opus-mt-en-hi")
-translation_tokenizer.save_pretrained("path/to/save/opus-mt-en-hi")
-
-# Upload to Hugging Face
-!huggingface-cli login
-!transformers-cli upload path/to/save/wav2vec2
-!transformers-cli upload path/to/save/opus-mt-en-hi
-
-from datetime import datetime
-import pytz
-
-def is_after_6_pm_ist():
-    ist = pytz.timezone('Asia/Kolkata')
-    current_time = datetime.now(ist)
-    return current_time.hour >= 18
-
-if is_after_6_pm_ist():
-    translation = translate_audio("path/to/audio/file.wav")
-    print(translation)
-else:
-    print("The translation service is available after 6 PM IST.")