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Whisper uses a Transformer-based encoder-decoder architecture optimized for speech recognition tasks. Understanding the architecture helps with model customization, debugging, and advanced use cases.

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Model Dimensions

Each Whisper model is defined by a ModelDimensions dataclass that specifies the architecture parameters:
whisper/model.py
@dataclass
class ModelDimensions:
    n_mels: int              # Number of mel frequency bins
    n_audio_ctx: int         # Audio context length
    n_audio_state: int       # Audio encoder hidden dimension
    n_audio_head: int        # Number of audio encoder attention heads
    n_audio_layer: int       # Number of audio encoder layers
    n_vocab: int             # Vocabulary size
    n_text_ctx: int          # Text context length
    n_text_state: int        # Text decoder hidden dimension
    n_text_head: int         # Number of text decoder attention heads
    n_text_layer: int        # Number of text decoder layers
These dimensions are automatically loaded from the model checkpoint and accessible via model.dims.

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Encoder-Decoder Structure

Whisper consists of two main components:

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Audio Encoder

The AudioEncoder processes mel spectrograms into audio features: 
class AudioEncoder(nn.Module):
    def __init__(
        self, n_mels: int, n_ctx: int, n_state: int, 
        n_head: int, n_layer: int
    ):
        super().__init__()
        # Two 1D convolution layers
        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
        
        # Sinusoidal positional embeddings
        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
        
        # Transformer blocks
        self.blocks = nn.ModuleList(
            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
        )
        self.ln_post = LayerNorm(n_state)

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Encoder Processing Steps

  1. Convolution Layers: Two 1D convolutions with GELU activation
    • First conv: kernel_size=3, padding=1 (preserves length)
    • Second conv: kernel_size=3, stride=2 (downsamples by 2x)
  2. Positional Encoding: Sinusoidal embeddings added to features
  3. Transformer Blocks: Self-attention and feedforward layers
  4. Layer Normalization: Final normalization layer 
def forward(self, x: Tensor):
    """
    x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
        the mel spectrogram of the audio
    """
    x = F.gelu(self.conv1(x))
    x = F.gelu(self.conv2(x))
    x = x.permute(0, 2, 1)
    
    assert x.shape[1:] == self.positional_embedding.shape
    x = (x + self.positional_embedding).to(x.dtype)
    
    for block in self.blocks:
        x = block(x)
    
    x = self.ln_post(x)
    return x

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Text Decoder

The TextDecoder generates text tokens autoregressively: 
class TextDecoder(nn.Module):
    def __init__(
        self, n_vocab: int, n_ctx: int, n_state: int,
        n_head: int, n_layer: int
    ):
        super().__init__()
        # Token and positional embeddings
        self.token_embedding = nn.Embedding(n_vocab, n_state)
        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
        
        # Transformer blocks with cross-attention
        self.blocks = nn.ModuleList([
            ResidualAttentionBlock(n_state, n_head, cross_attention=True)
            for _ in range(n_layer)
        ])
        self.ln = LayerNorm(n_state)
        
        # Causal mask for autoregressive generation
        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
        self.register_buffer("mask", mask, persistent=False)

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Decoder Processing Steps

  1. Token Embeddings: Convert token IDs to embeddings
  2. Positional Embeddings: Add position information
  3. Self-Attention: Attend to previous tokens (causal)
  4. Cross-Attention: Attend to encoder outputs
  5. Feedforward: Process through MLP
  6. Output Projection: Project to vocabulary logits
def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
    """
    x : torch.LongTensor, shape = (batch_size, <= n_ctx)
        the text tokens
    xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
        the encoded audio features to be attended on
    """
    offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
    x = (
        self.token_embedding(x)
        + self.positional_embedding[offset : offset + x.shape[-1]]
    )
    x = x.to(xa.dtype)
    
    for block in self.blocks:
        x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
    
    x = self.ln(x)
    logits = (
        x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
    ).float()
    
    return logits

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Attention Mechanism

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Multi-Head Attention

Whisper uses standard multi-head attention with optional scaled dot-product attention (SDPA) for efficiency: 
class MultiHeadAttention(nn.Module):
    def __init__(self, n_state: int, n_head: int):
        super().__init__()
        self.n_head = n_head
        self.query = Linear(n_state, n_state)
        self.key = Linear(n_state, n_state, bias=False)
        self.value = Linear(n_state, n_state)
        self.out = Linear(n_state, n_state)
The encoder uses self-attention where each position attends to all positions in the audio features:
# Self-attention in encoder
q = k = v = audio_features
attention_output = MultiHeadAttention(q, k, v)
The decoder uses causal self-attention where each token can only attend to previous tokens:
# Causal mask prevents attending to future tokens
mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
attention_output = MultiHeadAttention(q, k, v, mask=mask)
The decoder also uses cross-attention to attend to encoder outputs:
# Cross-attention: query from decoder, key/value from encoder
q = decoder_features
k = v = encoder_features
attention_output = MultiHeadAttention(q, k, v)

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Residual Attention Block

Each transformer layer is a ResidualAttentionBlock: 
class ResidualAttentionBlock(nn.Module):
    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
        super().__init__()
        
        # Self-attention
        self.attn = MultiHeadAttention(n_state, n_head)
        self.attn_ln = LayerNorm(n_state)
        
        # Cross-attention (decoder only)
        self.cross_attn = (
            MultiHeadAttention(n_state, n_head) if cross_attention else None
        )
        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
        
        # Feedforward MLP
        n_mlp = n_state * 4
        self.mlp = nn.Sequential(
            Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
        )
        self.mlp_ln = LayerNorm(n_state)

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Positional Encoding

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Sinusoidal Positional Embeddings (Encoder)

The encoder uses fixed sinusoidal positional embeddings: 
def sinusoids(length, channels, max_timescale=10000):
    """Returns sinusoids for positional embedding"""
    assert channels % 2 == 0
    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)

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Learned Positional Embeddings (Decoder)

The decoder uses learned positional embeddings: 
self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))

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Key-Value Caching

Whisper supports KV caching for efficient autoregressive generation: 
# Install KV cache hooks
cache, hooks = model.install_kv_cache_hooks()

# Decode with caching (much faster for long sequences)
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)

# Remove hooks when done
for hook in hooks:
    hook.remove()
KV caching stores previously computed key and value tensors, avoiding redundant computation during autoregressive decoding.

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Alignment Heads

Whisper uses specific attention heads for word-level timestamp alignment: 
# Set alignment heads from model checkpoint
model.set_alignment_heads(alignment_heads_bytes)

# Default: use last half of decoder layers
all_heads = torch.zeros(
    self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
)
all_heads[self.dims.n_text_layer // 2 :] = True
self.register_buffer("alignment_heads", all_heads.to_sparse())

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Model Properties

import whisper

model = whisper.load_model("turbo")

# Access model dimensions
print(f"Mel bins: {model.dims.n_mels}")
print(f"Audio context: {model.dims.n_audio_ctx}")
print(f"Audio state: {model.dims.n_audio_state}")
print(f"Text context: {model.dims.n_text_ctx}")
print(f"Vocabulary size: {model.dims.n_vocab}")

# Check model properties
print(f"Is multilingual: {model.is_multilingual}")
print(f"Number of languages: {model.num_languages}")
print(f"Device: {model.device}")

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Advanced Usage

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Custom Forward Pass

import whisper
import torch

model = whisper.load_model("turbo")

# Prepare audio
audio = whisper.load_audio("audio.mp3")
mel = whisper.log_mel_spectrogram(audio).unsqueeze(0).to(model.device)

# Manual encoding and decoding
audio_features = model.encoder(mel)
initial_tokens = torch.tensor([[50258, 50259, 50359, 50363]]).to(model.device)
logits = model.decoder(initial_tokens, audio_features)

# Get predictions
predicted_token = logits.argmax(dim=-1)
print(predicted_token)

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Accessing Internal Components

# Access encoder
encoder = model.encoder
print(f"Encoder layers: {len(encoder.blocks)}")

# Access decoder
decoder = model.decoder
print(f"Decoder layers: {len(decoder.blocks)}")

# Iterate through attention blocks
for i, block in enumerate(decoder.blocks):
    print(f"Block {i}: {block.attn.n_head} heads")

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Performance Optimizations

Scaled Dot-Product Attention

Uses PyTorch’s optimized SDPA when available for faster attention computation.

KV Caching

Caches key and value tensors to avoid redundant computation during generation.

Mixed Precision

Supports FP16/BF16 for reduced memory usage and faster inference.

Batch Processing

Can process multiple audio samples in parallel for improved throughput.
For production deployments, consider using the turbo model with KV caching enabled for optimal speed.

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