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Created October 13, 2022 22:06
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Internal Language Model Subtraction
def fast_beam_search(
model: Transducer,
decoding_graph: k2.Fsa,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
beam: float,
max_states: int,
max_contexts: int,
temperature: float = 1.0,
ILM_lambda: float = 0.0,
) -> k2.Fsa:
"""It limits the maximum number of symbols per frame to 1.
Args:
model:
An instance of `Transducer`.
decoding_graph:
Decoding graph used for decoding, may be a TrivialGraph or a LG.
encoder_out:
A tensor of shape (N, T, C) from the encoder.
encoder_out_lens:
A tensor of shape (N,) containing the number of frames in `encoder_out`
before padding.
beam:
Beam value, similar to the beam used in Kaldi..
max_states:
Max states per stream per frame.
max_contexts:
Max contexts pre stream per frame.
temperature:
Softmax temperature.
Returns:
Return an FsaVec with axes [utt][state][arc] containing the decoded
lattice. Note: When the input graph is a TrivialGraph, the returned
lattice is actually an acceptor.
"""
assert encoder_out.ndim == 3
context_size = model.decoder.context_size
vocab_size = model.decoder.vocab_size
B, T, C = encoder_out.shape
config = k2.RnntDecodingConfig(
vocab_size=vocab_size,
decoder_history_len=context_size,
beam=beam,
max_contexts=max_contexts,
max_states=max_states,
)
individual_streams = []
for i in range(B):
individual_streams.append(k2.RnntDecodingStream(decoding_graph))
decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
encoder_out = model.joiner.encoder_proj(encoder_out)
for t in range(T):
# shape is a RaggedShape of shape (B, context)
# contexts is a Tensor of shape (shape.NumElements(), context_size)
shape, contexts = decoding_streams.get_contexts()
# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
contexts = contexts.to(torch.int64)
# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
decoder_out = model.decoder(contexts, need_pad=False)
decoder_out = model.joiner.decoder_proj(decoder_out)
# current_encoder_out is of shape
# (shape.NumElements(), 1, joiner_dim)
# fmt: off
current_encoder_out = torch.index_select(
encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
)
# fmt: on
logits = model.joiner(
current_encoder_out.unsqueeze(2),
decoder_out.unsqueeze(1),
project_input=False,
)
logits = logits.squeeze(1).squeeze(1)
log_probs = (logits / temperature).log_softmax(dim=-1)
# Internal LM subtraction
current_ILM_encoder_out = torch.zeros_like(current_encoder_out)
ILM_logits = model.joiner(current_ILM_encoder_out, decoder_out, project=False)
ILM_logits = ILM_logits.squeeze(1).squeeze(1)
ILM_log_probs = ILM_logits.log_softmax(dim=-1)
log_probs -= ILM_log_probs * ILM_lambda
decoding_streams.advance(log_probs)
decoding_streams.terminate_and_flush_to_streams()
lattice = decoding_streams.format_output(encoder_out_lens.tolist())
return lattice
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