mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-19 05:54:20 +00:00
modified beam search for stateless3,4
This commit is contained in:
pkufool
parent
72d76a4ff8
commit
353863a55c
@ -0,0 +1 @@
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../pruned_transducer_stateless2/streaming_beam_search.py
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@ -44,6 +44,11 @@ from decode_stream import DecodeStream
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from kaldifeat import Fbank, FbankOptions
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from lhotse import CutSet
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from librispeech import LibriSpeech
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from streaming_beam_search import (
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fast_beam_search_one_best,
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greedy_search,
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modified_beam_search,
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)
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from torch.nn.utils.rnn import pad_sequence
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from train import add_model_arguments, get_params, get_transducer_model
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@ -52,10 +57,8 @@ from icefall.checkpoint import (
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.decode import one_best_decoding
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from icefall.utils import (
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AttributeDict,
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get_texts,
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setup_logger,
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store_transcripts,
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write_error_stats,
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@ -115,10 +118,21 @@ def get_parser():
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="""Support only greedy_search and fast_beam_search now.
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help="""Supported decoding methods are:
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greedy_search
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modified_beam_search
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fast_beam_search
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=4,
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help="""An interger indicating how many candidates we will keep for each
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frame. Used only when --decoding-method is modified_beam_search.""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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@ -186,109 +200,6 @@ def get_parser():
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return parser
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def greedy_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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streams: List[DecodeStream],
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) -> List[List[int]]:
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assert len(streams) == encoder_out.size(0)
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assert encoder_out.ndim == 3
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blank_id = model.decoder.blank_id
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context_size = model.decoder.context_size
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device = model.device
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T = encoder_out.size(1)
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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# decoder_out is of shape (N, decoder_out_dim)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# logging.info(f"decoder_out shape : {decoder_out.shape}")
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for t in range(T):
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# current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
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current_encoder_out = encoder_out[:, t : t + 1, :] # noqa
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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project_input=False,
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)
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# logits'shape (batch_size, vocab_size)
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logits = logits.squeeze(1).squeeze(1)
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assert logits.ndim == 2, logits.shape
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y = logits.argmax(dim=1).tolist()
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emitted = False
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for i, v in enumerate(y):
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if v != blank_id:
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streams[i].hyp.append(v)
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emitted = True
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if emitted:
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# update decoder output
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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decoder_out = model.decoder(
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decoder_input,
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need_pad=False,
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)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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hyp_tokens = []
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for stream in streams:
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hyp_tokens.append(stream.hyp)
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return hyp_tokens
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def fast_beam_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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processed_lens: torch.Tensor,
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decoding_streams: k2.RnntDecodingStreams,
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) -> List[List[int]]:
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B, T, C = encoder_out.shape
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for t in range(T):
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# shape is a RaggedShape of shape (B, context)
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# contexts is a Tensor of shape (shape.NumElements(), context_size)
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shape, contexts = decoding_streams.get_contexts()
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# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
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contexts = contexts.to(torch.int64)
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# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
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decoder_out = model.decoder(contexts, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# current_encoder_out is of shape
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# (shape.NumElements(), 1, joiner_dim)
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# fmt: off
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current_encoder_out = torch.index_select(
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encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
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)
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# fmt: on
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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project_input=False,
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)
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logits = logits.squeeze(1).squeeze(1)
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log_probs = logits.log_softmax(dim=-1)
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decoding_streams.advance(log_probs)
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decoding_streams.terminate_and_flush_to_streams()
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lattice = decoding_streams.format_output(processed_lens.tolist())
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best_path = one_best_decoding(lattice)
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hyp_tokens = get_texts(best_path)
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return hyp_tokens
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def decode_one_chunk(
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params: AttributeDict,
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model: nn.Module,
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@ -313,7 +224,6 @@ def decode_one_chunk(
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feature_lens = []
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states = []
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rnnt_stream_list = []
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processed_lens = []
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for stream in decode_streams:
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@ -324,8 +234,6 @@ def decode_one_chunk(
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feature_lens.append(feat_len)
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states.append(stream.states)
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processed_lens.append(stream.done_frames)
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if params.decoding_method == "fast_beam_search":
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rnnt_stream_list.append(stream.rnnt_decoding_stream)
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feature_lens = torch.tensor(feature_lens, device=device)
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features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
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@ -337,19 +245,13 @@ def decode_one_chunk(
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# frames.
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tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
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if features.size(1) < tail_length:
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feature_lens += tail_length - features.size(1)
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features = torch.cat(
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[
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pad_length = tail_length - features.size(1)
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feature_lens += pad_length
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features = torch.nn.functional.pad(
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features,
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torch.tensor(
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LOG_EPS, dtype=features.dtype, device=device
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).expand(
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features.size(0),
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tail_length - features.size(1),
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features.size(2),
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),
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],
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dim=1,
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(0, 0, 0, pad_length),
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mode="constant",
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value=LOG_EPS,
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)
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states = [
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@ -370,22 +272,31 @@ def decode_one_chunk(
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encoder_out = model.joiner.encoder_proj(encoder_out)
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if params.decoding_method == "greedy_search":
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hyp_tokens = greedy_search(model, encoder_out, decode_streams)
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elif params.decoding_method == "fast_beam_search":
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config = k2.RnntDecodingConfig(
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vocab_size=params.vocab_size,
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decoder_history_len=params.context_size,
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beam=params.beam,
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max_contexts=params.max_contexts,
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max_states=params.max_states,
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greedy_search(
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model=model, encoder_out=encoder_out, streams=decode_streams
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)
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decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config)
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elif params.decoding_method == "fast_beam_search":
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processed_lens = processed_lens + encoder_out_lens
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hyp_tokens = fast_beam_search(
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model, encoder_out, processed_lens, decoding_streams
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fast_beam_search_one_best(
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model=model,
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encoder_out=encoder_out,
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processed_lens=processed_lens,
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streams=decode_streams,
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beam=params.beam,
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max_states=params.max_states,
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max_contexts=params.max_contexts,
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)
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elif params.decoding_method == "modified_beam_search":
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modified_beam_search(
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model=model,
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streams=decode_streams,
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encoder_out=encoder_out,
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beam=params.beam_size,
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)
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else:
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assert False
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raise ValueError(
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f"Unsupported decoding method: {params.decoding_method}"
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)
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states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
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@ -393,8 +304,6 @@ def decode_one_chunk(
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for i in range(len(decode_streams)):
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decode_streams[i].states = [states[0][i], states[1][i]]
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decode_streams[i].done_frames += encoder_out_lens[i]
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if params.decoding_method == "fast_beam_search":
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decode_streams[i].hyp = hyp_tokens[i]
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if decode_streams[i].done:
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finished_streams.append(i)
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@ -478,13 +387,10 @@ def decode_dataset(
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params=params, model=model, decode_streams=decode_streams
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)
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for i in sorted(finished_streams, reverse=True):
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hyp = decode_streams[i].hyp
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if params.decoding_method == "greedy_search":
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hyp = hyp[params.context_size :] # noqa
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decode_results.append(
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(
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decode_streams[i].ground_truth.split(),
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sp.decode(hyp).split(),
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sp.decode(decode_streams[i].decoding_result()).split(),
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)
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)
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del decode_streams[i]
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@ -498,24 +404,28 @@ def decode_dataset(
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params=params, model=model, decode_streams=decode_streams
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)
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for i in sorted(finished_streams, reverse=True):
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hyp = decode_streams[i].hyp
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if params.decoding_method == "greedy_search":
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hyp = hyp[params.context_size :] # noqa
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decode_results.append(
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(
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decode_streams[i].ground_truth.split(),
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sp.decode(hyp).split(),
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sp.decode(decode_streams[i].decoding_result()).split(),
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)
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)
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del decode_streams[i]
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if params.decoding_method == "greedy_search":
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key = "greedy_search"
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if params.decoding_method == "fast_beam_search":
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elif params.decoding_method == "fast_beam_search":
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key = (
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f"beam_{params.beam}_"
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f"max_contexts_{params.max_contexts}_"
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f"max_states_{params.max_states}"
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)
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elif params.decoding_method == "modified_beam_search":
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key = f"beam_size_{params.beam_size}"
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else:
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raise ValueError(
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f"Unsupported decoding method: {params.decoding_method}"
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)
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return {key: decode_results}
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@ -0,0 +1 @@
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../pruned_transducer_stateless2/streaming_beam_search.py
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@ -43,6 +43,11 @@ from asr_datamodule import LibriSpeechAsrDataModule
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from decode_stream import DecodeStream
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from kaldifeat import Fbank, FbankOptions
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from lhotse import CutSet
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from streaming_beam_search import (
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fast_beam_search_one_best,
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greedy_search,
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modified_beam_search,
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)
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from torch.nn.utils.rnn import pad_sequence
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from train import add_model_arguments, get_params, get_transducer_model
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@ -52,10 +57,8 @@ from icefall.checkpoint import (
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.decode import one_best_decoding
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from icefall.utils import (
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AttributeDict,
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get_texts,
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setup_logger,
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store_transcripts,
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str2bool,
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@ -127,10 +130,21 @@ def get_parser():
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="""Support only greedy_search and fast_beam_search now.
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help="""Supported decoding methods are:
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greedy_search
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modified_beam_search
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fast_beam_search
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=4,
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help="""An interger indicating how many candidates we will keep for each
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frame. Used only when --decoding-method is modified_beam_search.""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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@ -198,109 +212,6 @@ def get_parser():
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return parser
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def greedy_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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streams: List[DecodeStream],
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) -> List[List[int]]:
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assert len(streams) == encoder_out.size(0)
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assert encoder_out.ndim == 3
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blank_id = model.decoder.blank_id
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context_size = model.decoder.context_size
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device = model.device
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T = encoder_out.size(1)
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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# decoder_out is of shape (N, decoder_out_dim)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# logging.info(f"decoder_out shape : {decoder_out.shape}")
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for t in range(T):
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# current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
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current_encoder_out = encoder_out[:, t : t + 1, :] # noqa
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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project_input=False,
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)
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# logits'shape (batch_size, vocab_size)
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logits = logits.squeeze(1).squeeze(1)
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assert logits.ndim == 2, logits.shape
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y = logits.argmax(dim=1).tolist()
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emitted = False
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for i, v in enumerate(y):
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if v != blank_id:
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streams[i].hyp.append(v)
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emitted = True
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if emitted:
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# update decoder output
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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decoder_out = model.decoder(
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decoder_input,
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need_pad=False,
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)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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hyp_tokens = []
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for stream in streams:
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hyp_tokens.append(stream.hyp)
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return hyp_tokens
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def fast_beam_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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processed_lens: torch.Tensor,
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decoding_streams: k2.RnntDecodingStreams,
|
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) -> List[List[int]]:
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B, T, C = encoder_out.shape
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for t in range(T):
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# shape is a RaggedShape of shape (B, context)
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# contexts is a Tensor of shape (shape.NumElements(), context_size)
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shape, contexts = decoding_streams.get_contexts()
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# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
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contexts = contexts.to(torch.int64)
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# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
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decoder_out = model.decoder(contexts, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# current_encoder_out is of shape
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# (shape.NumElements(), 1, joiner_dim)
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# fmt: off
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current_encoder_out = torch.index_select(
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encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
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)
|
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# fmt: on
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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project_input=False,
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)
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logits = logits.squeeze(1).squeeze(1)
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log_probs = logits.log_softmax(dim=-1)
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decoding_streams.advance(log_probs)
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decoding_streams.terminate_and_flush_to_streams()
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lattice = decoding_streams.format_output(processed_lens.tolist())
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best_path = one_best_decoding(lattice)
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hyp_tokens = get_texts(best_path)
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return hyp_tokens
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|
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def decode_one_chunk(
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params: AttributeDict,
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model: nn.Module,
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@ -325,7 +236,6 @@ def decode_one_chunk(
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feature_lens = []
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states = []
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rnnt_stream_list = []
|
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processed_lens = []
|
||||
|
||||
for stream in decode_streams:
|
||||
@ -336,8 +246,6 @@ def decode_one_chunk(
|
||||
feature_lens.append(feat_len)
|
||||
states.append(stream.states)
|
||||
processed_lens.append(stream.done_frames)
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
rnnt_stream_list.append(stream.rnnt_decoding_stream)
|
||||
|
||||
feature_lens = torch.tensor(feature_lens, device=device)
|
||||
features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
|
||||
@ -349,19 +257,13 @@ def decode_one_chunk(
|
||||
# frames.
|
||||
tail_length = 7 + (2 + params.right_context) * params.subsampling_factor
|
||||
if features.size(1) < tail_length:
|
||||
feature_lens += tail_length - features.size(1)
|
||||
features = torch.cat(
|
||||
[
|
||||
pad_length = tail_length - features.size(1)
|
||||
feature_lens += pad_length
|
||||
features = torch.nn.functional.pad(
|
||||
features,
|
||||
torch.tensor(
|
||||
LOG_EPS, dtype=features.dtype, device=device
|
||||
).expand(
|
||||
features.size(0),
|
||||
tail_length - features.size(1),
|
||||
features.size(2),
|
||||
),
|
||||
],
|
||||
dim=1,
|
||||
(0, 0, 0, pad_length),
|
||||
mode="constant",
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
states = [
|
||||
@ -382,22 +284,31 @@ def decode_one_chunk(
|
||||
encoder_out = model.joiner.encoder_proj(encoder_out)
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp_tokens = greedy_search(model, encoder_out, decode_streams)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
config = k2.RnntDecodingConfig(
|
||||
vocab_size=params.vocab_size,
|
||||
decoder_history_len=params.context_size,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
greedy_search(
|
||||
model=model, encoder_out=encoder_out, streams=decode_streams
|
||||
)
|
||||
decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
processed_lens = processed_lens + encoder_out_lens
|
||||
hyp_tokens = fast_beam_search(
|
||||
model, encoder_out, processed_lens, decoding_streams
|
||||
fast_beam_search_one_best(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
processed_lens=processed_lens,
|
||||
streams=decode_streams,
|
||||
beam=params.beam,
|
||||
max_states=params.max_states,
|
||||
max_contexts=params.max_contexts,
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
modified_beam_search(
|
||||
model=model,
|
||||
streams=decode_streams,
|
||||
encoder_out=encoder_out,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
assert False
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
|
||||
states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
|
||||
|
||||
@ -405,8 +316,6 @@ def decode_one_chunk(
|
||||
for i in range(len(decode_streams)):
|
||||
decode_streams[i].states = [states[0][i], states[1][i]]
|
||||
decode_streams[i].done_frames += encoder_out_lens[i]
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decode_streams[i].hyp = hyp_tokens[i]
|
||||
if decode_streams[i].done:
|
||||
finished_streams.append(i)
|
||||
|
||||
@ -490,13 +399,10 @@ def decode_dataset(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :] # noqa
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
sp.decode(decode_streams[i].decoding_result()).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
@ -510,24 +416,28 @@ def decode_dataset(
|
||||
params=params, model=model, decode_streams=decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :] # noqa
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
sp.decode(decode_streams[i].decoding_result()).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
key = "greedy_search"
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
key = (
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
)
|
||||
elif params.decoding_method == "modified_beam_search":
|
||||
key = f"beam_size_{params.beam_size}"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
return {key: decode_results}
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user