```python # Whisper Model Pseudocode # 1. Audio Preprocessing def preprocess_audio(audio): pcm = convert_to_pcm(audio) mel_spectrogram = compute_mel_spectrogram(pcm) return mel_spectrogram # 2. Encoder def encoder(mel_spectrogram): # Apply convolutional layers features = conv_layers(mel_spectrogram) # Apply transformer blocks for block in encoder_blocks: features = self_attention(features) + features # Residual connection features = feed_forward(features) + features # Residual connection return features # 3. Decoder def decoder(encoder_features, previous_tokens): # Initialize with start token tokens = [START_TOKEN] + previous_tokens while not end_of_sequence: # Embed tokens token_embeddings = embed(tokens) # Apply transformer blocks for block in decoder_blocks: # Self-attention on token embeddings x = self_attention(token_embeddings) + token_embeddings # Cross-attention with encoder features x = cross_attention(x, encoder_features) + x # Feed-forward x = feed_forward(x) + x # Project to vocabulary logits = linear_projection(x) # Select next token (e.g., greedy or sampling) next_token = select_token(logits) tokens.append(next_token) if next_token == END_TOKEN: break return tokens # 4. Main Whisper Flow def whisper(audio): mel_spectrogram = preprocess_audio(audio) encoder_features = encoder(mel_spectrogram) transcription = decoder(encoder_features, []) return convert_tokens_to_text(transcription) # Attention mechanism (simplified) def attention(query, key, value): scores = dot_product(query, key) attention_weights = softmax(scores) return dot_product(attention_weights, value) ``` #programming #code #artificial-intelligence