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"""Export Parakeet Realtime EOU RNNT components into CoreML.
This model uses a cache-aware streaming FastConformer encoder.
The encoder requires splitting into:
1. Initial encoder (no cache, for first chunk)
2. Streaming encoder (with cache inputs/outputs)
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import coremltools as ct
import torch
@dataclass
class ExportSettings:
output_dir: Path
compute_units: ct.ComputeUnit
deployment_target: Optional[ct.target]
compute_precision: Optional[ct.precision]
max_audio_seconds: float
max_symbol_steps: int
# Streaming-specific settings
chunk_size_frames: int # Number of frames per chunk (after subsampling)
cache_size: int # Size of the channel cache
class PreprocessorWrapper(torch.nn.Module):
"""Wrapper for the preprocessor (mel spectrogram extraction)."""
def __init__(self, module: torch.nn.Module) -> None:
super().__init__()
self.module = module
def forward(
self, audio_signal: torch.Tensor, length: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
mel, mel_length = self.module(
input_signal=audio_signal, length=length.to(dtype=torch.long)
)
return mel, mel_length
class EncoderInitialWrapper(torch.nn.Module):
"""Encoder wrapper for the initial chunk (no cache input).
This is used for the first chunk of audio where there's no previous cache.
It outputs the encoder features and initial cache states.
"""
def __init__(self, module: torch.nn.Module) -> None:
super().__init__()
self.module = module
def forward(
self, features: torch.Tensor, length: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward pass for initial chunk without cache.
Args:
features: Mel spectrogram [B, D, T]
length: Sequence lengths [B]
Returns:
encoded: Encoder output [B, D, T_enc]
encoded_lengths: Output lengths [B]
"""
# Initial forward without cache
encoded, encoded_lengths = self.module(
audio_signal=features, length=length.to(dtype=torch.long)
)
return encoded, encoded_lengths
class EncoderStreamingWrapper(torch.nn.Module):
"""Encoder wrapper for streaming with cache.
This is used for subsequent chunks where cache states are available.
It takes cache states as input and outputs updated cache states.
"""
def __init__(self, module: torch.nn.Module) -> None:
super().__init__()
self.module = module
def forward(
self,
features: torch.Tensor,
length: torch.Tensor,
cache_last_channel: torch.Tensor,
cache_last_time: torch.Tensor,
cache_last_channel_len: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Forward pass with cache for streaming."""
# Transpose caches from [B, L, ...] to [L, B, ...] for NeMo
cache_last_channel_t = cache_last_channel.transpose(0, 1)
cache_last_time_t = cache_last_time.transpose(0, 1)
cache_len_i64 = cache_last_channel_len.to(dtype=torch.int64)
# Call encoder forward with cache parameters
encoded, encoded_lengths, cache_ch_next, cache_t_next, cache_len_next = self.module(
audio_signal=features,
length=length.to(dtype=torch.long),
cache_last_channel=cache_last_channel_t,
cache_last_time=cache_last_time_t,
cache_last_channel_len=cache_len_i64,
)
# Transpose caches back from [L, B, ...] to [B, L, ...]
cache_ch_next = cache_ch_next.transpose(0, 1)
cache_t_next = cache_t_next.transpose(0, 1)
return (
encoded,
encoded_lengths.to(dtype=torch.int32),
cache_ch_next,
cache_t_next,
cache_len_next.to(dtype=torch.int32),
)
class DecoderWrapper(torch.nn.Module):
"""Wrapper for the RNNT prediction network (decoder)."""
def __init__(self, module: torch.nn.Module) -> None:
super().__init__()
self.module = module
def forward(
self,
targets: torch.Tensor,
target_lengths: torch.Tensor,
h_in: torch.Tensor,
c_in: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
state = [h_in, c_in]
decoder_output, _, new_state = self.module(
targets=targets.to(dtype=torch.long),
target_length=target_lengths.to(dtype=torch.long),
states=state,
)
return decoder_output, new_state[0], new_state[1]
class JointWrapper(torch.nn.Module):
"""Wrapper for the RNNT joint network."""
def __init__(self, module: torch.nn.Module) -> None:
super().__init__()
self.module = module
def forward(
self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor
) -> torch.Tensor:
# Input: encoder_outputs [B, D, T], decoder_outputs [B, D, U]
# Transpose to match what projection layers expect
encoder_outputs = encoder_outputs.transpose(1, 2) # [B, T, D]
decoder_outputs = decoder_outputs.transpose(1, 2) # [B, U, D]
# Apply projections
enc_proj = self.module.enc(encoder_outputs) # [B, T, joint_dim]
dec_proj = self.module.pred(decoder_outputs) # [B, U, joint_dim]
# Explicit broadcasting along T and U
x = enc_proj.unsqueeze(2) + dec_proj.unsqueeze(1) # [B, T, U, joint_dim]
x = self.module.joint_net[0](x) # ReLU
x = self.module.joint_net[1](x) # Dropout (no-op in eval)
out = self.module.joint_net[2](x) # Linear -> logits
return out
class MelEncoderWrapper(torch.nn.Module):
"""Fused wrapper: waveform -> mel -> encoder (no cache, initial chunk)."""
def __init__(
self, preprocessor: PreprocessorWrapper, encoder: EncoderInitialWrapper
) -> None:
super().__init__()
self.preprocessor = preprocessor
self.encoder = encoder
def forward(
self, audio_signal: torch.Tensor, audio_length: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
mel, mel_length = self.preprocessor(audio_signal, audio_length)
encoded, enc_len = self.encoder(mel, mel_length.to(dtype=torch.int32))
return encoded, enc_len
class MelEncoderStreamingWrapper(torch.nn.Module):
"""Fused wrapper: waveform -> mel -> encoder (with cache, streaming)."""
def __init__(
self, preprocessor: PreprocessorWrapper, encoder: EncoderStreamingWrapper
) -> None:
super().__init__()
self.preprocessor = preprocessor
self.encoder = encoder
def forward(
self,
audio_signal: torch.Tensor,
audio_length: torch.Tensor,
cache_last_channel: torch.Tensor,
cache_last_time: torch.Tensor,
cache_last_channel_len: torch.Tensor,
) -> Tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
]:
mel, mel_length = self.preprocessor(audio_signal, audio_length)
return self.encoder(
mel,
mel_length.to(dtype=torch.int32),
cache_last_channel,
cache_last_time,
cache_last_channel_len,
)
class JointDecisionWrapper(torch.nn.Module):
"""Joint + decision head: outputs label id, label prob.
Unlike TDT, EOU models don't have duration outputs.
They have a special EOU token that marks end of utterance.
"""
def __init__(self, joint: JointWrapper, vocab_size: int) -> None:
super().__init__()
self.joint = joint
self.vocab_with_blank = int(vocab_size) + 1
def forward(
self, encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
logits = self.joint(encoder_outputs, decoder_outputs)
token_logits = logits[..., : self.vocab_with_blank]
# Token selection
token_ids = torch.argmax(token_logits, dim=-1).to(dtype=torch.int32)
token_probs_all = torch.softmax(token_logits, dim=-1)
token_prob = torch.gather(
token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1)
).squeeze(-1)
return token_ids, token_prob
class JointDecisionSingleStep(torch.nn.Module):
"""Single-step variant for streaming: encoder_step [1, D, 1] -> [1,1,1].
Inputs:
- encoder_step: [B=1, D, T=1]
- decoder_step: [B=1, D, U=1]
Returns:
- token_id: [1, 1, 1] int32
- token_prob: [1, 1, 1] float32
- top_k_ids: [1, 1, 1, K] int32
- top_k_logits: [1, 1, 1, K] float32
"""
def __init__(self, joint: JointWrapper, vocab_size: int, top_k: int = 64) -> None:
super().__init__()
self.joint = joint
self.vocab_with_blank = int(vocab_size) + 1
self.top_k = int(top_k)
def forward(
self, encoder_step: torch.Tensor, decoder_step: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
logits = self.joint(encoder_step, decoder_step) # [1, 1, 1, V]
token_logits = logits[..., : self.vocab_with_blank]
token_ids = torch.argmax(token_logits, dim=-1, keepdim=False).to(
dtype=torch.int32
)
token_probs_all = torch.softmax(token_logits, dim=-1)
token_prob = torch.gather(
token_probs_all, dim=-1, index=token_ids.long().unsqueeze(-1)
).squeeze(-1)
# Top-K candidates for host-side re-ranking
topk_logits, topk_ids_long = torch.topk(
token_logits, k=min(self.top_k, token_logits.shape[-1]), dim=-1
)
topk_ids = topk_ids_long.to(dtype=torch.int32)
return token_ids, token_prob, topk_ids, topk_logits
def _coreml_convert(
traced: torch.jit.ScriptModule,
inputs,
outputs,
settings: ExportSettings,
compute_units_override: Optional[ct.ComputeUnit] = None,
) -> ct.models.MLModel:
cu = (
compute_units_override
if compute_units_override is not None
else settings.compute_units
)
kwargs = {
"convert_to": "mlprogram",
"inputs": inputs,
"outputs": outputs,
"compute_units": cu,
}
print("Converting:", traced.__class__.__name__)
print("Conversion kwargs:", kwargs)
if settings.deployment_target is not None:
kwargs["minimum_deployment_target"] = settings.deployment_target
if settings.compute_precision is not None:
kwargs["compute_precision"] = settings.compute_precision
return ct.convert(traced, **kwargs)
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