Add modified_beam_search for pruned_transducer_stateless/streaming_decode.py

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -751,7 +751,7 @@ class HypothesisList(object):
         return ", ".join(s)
 
 
-def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
+def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
     """Return a ragged shape with axes [utt][num_hyps].
 
     Args:
@@ -847,7 +847,7 @@ def modified_beam_search(
         finalized_B = B[batch_size:] + finalized_B
         B = B[:batch_size]
 
-        hyps_shape = _get_hyps_shape(B).to(device)
+        hyps_shape = get_hyps_shape(B).to(device)
 
         A = [list(b) for b in B]
         B = [HypothesisList() for _ in range(batch_size)]

egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py

@@ -19,6 +19,7 @@ from typing import List, Optional, Tuple
 
 import k2
 import torch
+from beam_search import Hypothesis, HypothesisList
 
 from icefall.utils import AttributeDict
 
@@ -42,7 +43,8 @@ class DecodeStream(object):
           device:
             The device to run this stream.
         """
-        if decoding_graph is not None:
+        if params.decoding_method == "fast_beam_search":
+            assert decoding_graph is not None
             assert device == decoding_graph.device
 
         self.params = params
@@ -77,15 +79,23 @@ class DecodeStream(object):
 
         if params.decoding_method == "greedy_search":
             self.hyp = [params.blank_id] * params.context_size
+        elif params.decoding_method == "modified_beam_search":
+            self.hyps = HypothesisList()
+            self.hyps.add(
+                Hypothesis(
+                    ys=[params.blank_id] * params.context_size,
+                    log_prob=torch.zeros(1, dtype=torch.float32, device=device),
+                )
+            )
         elif params.decoding_method == "fast_beam_search":
             # The rnnt_decoding_stream for fast_beam_search.
             self.rnnt_decoding_stream: k2.RnntDecodingStream = (
                 k2.RnntDecodingStream(decoding_graph)
             )
         else:
-            assert (
-                False
-            ), f"Decoding method :{params.decoding_method} do not support."
+            raise ValueError(
+                f"Unsupported decoding method: {params.decoding_method}"
+            )
 
     @property
     def done(self) -> bool:
@@ -124,3 +134,14 @@ class DecodeStream(object):
             self._done = True
 
         return ret_features, ret_length
+
+    def decoding_result(self) -> List[int]:
+        """Obtain current decoding result."""
+        if self.params.decoding_method == "greedy_search":
+            return self.hyp[self.params.context_size :]  # noqa
+        elif self.params.decoding_method == "modified_beam_search":
+            best_hyp = self.hyps.get_most_probable(length_norm=True)
+            return best_hyp.ys[self.params.context_size :]  # noqa
+        else:
+            assert self.params.decoding_method == "fast_beam_search"
+            return self.hyp

egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py

@@ -31,6 +31,7 @@ Usage:
 import argparse
 import logging
 import math
+import warnings
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
@@ -40,6 +41,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
+from beam_search import Hypothesis, HypothesisList, get_hyps_shape
 from decode_stream import DecodeStream
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
@@ -114,10 +116,21 @@ def get_parser():
         "--decoding-method",
         type=str,
         default="greedy_search",
-        help="""Support only greedy_search and fast_beam_search now.
+        help="""Supported decoding methods are:
+        greedy_search
+        modified_beam_search
+        fast_beam_search
         """,
     )
 
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An interger indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is modified_beam_search.""",
+    )
+
     parser.add_argument(
         "--beam",
         type=float,
@@ -189,8 +202,17 @@ def greedy_search(
     model: nn.Module,
     encoder_out: torch.Tensor,
     streams: List[DecodeStream],
-) -> List[List[int]]:
+) -> None:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
 
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        Output from the encoder. Its shape is (N, T, C), where N >= 1.
+      streams:
+        A list of Stream objects.
+    """
     assert len(streams) == encoder_out.size(0)
     assert encoder_out.ndim == 3
 
@@ -237,20 +259,163 @@ def greedy_search(
                 need_pad=False,
             )
 
-    hyp_tokens = []
-    for stream in streams:
-        hyp_tokens.append(stream.hyp)
-    return hyp_tokens
+
+def modified_beam_search(
+    model: nn.Module,
+    encoder_out: torch.Tensor,
+    streams: List[DecodeStream],
+    beam: int = 4,
+):
+    """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
+
+    Args:
+      model:
+        The RNN-T model.
+      encoder_out:
+        A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of
+        the encoder model.
+      streams:
+        A list of stream objects.
+      beam:
+        Number of active paths during the beam search.
+    """
+    assert encoder_out.ndim == 3, encoder_out.shape
+    assert len(streams) == encoder_out.size(0)
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+    device = next(model.parameters()).device
+    batch_size = len(streams)
+    T = encoder_out.size(1)
+
+    B = [stream.hyps for stream in streams]
+
+    for t in range(T):
+        current_encoder_out = encoder_out[:, t].unsqueeze(1).unsqueeze(1)
+        # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
+
+        hyps_shape = get_hyps_shape(B).to(device)
+
+        A = [list(b) for b in B]
+        B = [HypothesisList() for _ in range(batch_size)]
+
+        ys_log_probs = torch.stack(
+            [hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0
+        )  # (num_hyps, 1)
+
+        decoder_input = torch.tensor(
+            [hyp.ys[-context_size:] for hyps in A for hyp in hyps],
+            device=device,
+            dtype=torch.int64,
+        )  # (num_hyps, context_size)
+
+        decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
+        # decoder_out is of shape (num_hyps, 1, 1, decoder_output_dim)
+
+        # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
+        # as index, so we use `to(torch.int64)` below.
+        current_encoder_out = torch.index_select(
+            current_encoder_out,
+            dim=0,
+            index=hyps_shape.row_ids(1).to(torch.int64),
+        )  # (num_hyps, encoder_out_dim)
+
+        logits = model.joiner(current_encoder_out, decoder_out)
+        # logits is of shape (num_hyps, 1, 1, vocab_size)
+
+        logits = logits.squeeze(1).squeeze(1)
+
+        log_probs = logits.log_softmax(dim=-1)  # (num_hyps, vocab_size)
+
+        log_probs.add_(ys_log_probs)
+
+        vocab_size = log_probs.size(-1)
+
+        log_probs = log_probs.reshape(-1)
+
+        row_splits = hyps_shape.row_splits(1) * vocab_size
+        log_probs_shape = k2.ragged.create_ragged_shape2(
+            row_splits=row_splits, cached_tot_size=log_probs.numel()
+        )
+        ragged_log_probs = k2.RaggedTensor(
+            shape=log_probs_shape, value=log_probs
+        )
+
+        for i in range(batch_size):
+            topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
+                topk_token_indexes = (topk_indexes % vocab_size).tolist()
+
+            for k in range(len(topk_hyp_indexes)):
+                hyp_idx = topk_hyp_indexes[k]
+                hyp = A[i][hyp_idx]
+
+                new_ys = hyp.ys[:]
+                new_token = topk_token_indexes[k]
+                if new_token != blank_id:
+                    new_ys.append(new_token)
+
+                new_log_prob = topk_log_probs[k]
+                new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
+                B[i].add(new_hyp)
+
+    for i in range(batch_size):
+        streams[i].hyps = B[i]
 
 
-def fast_beam_search(
+def fast_beam_search_one_best(
     model: nn.Module,
     encoder_out: torch.Tensor,
     processed_lens: torch.Tensor,
-    decoding_streams: k2.RnntDecodingStreams,
-) -> List[List[int]]:
+    streams: List[DecodeStream],
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> None:
+    """It limits the maximum number of symbols per frame to 1.
 
+    A lattice is first generated by Fsa-based beam search, then we get the
+    recognition by applying shortest path on the lattice.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      processed_lens:
+        A tensor of shape (N,) containing the number of processed frames
+        in `encoder_out` before padding.
+      streams:
+        A list of stream objects.
+      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.
+    """
+    assert encoder_out.ndim == 3
     B, T, C = encoder_out.shape
+    assert B == len(streams)
+
+    context_size = model.decoder.context_size
+    vocab_size = model.decoder.vocab_size
+
+    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(streams[i].rnnt_decoding_stream)
+    decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
+
     for t in range(T):
         # shape is a RaggedShape of shape (B, context)
         # contexts is a Tensor of shape (shape.NumElements(), context_size)
@@ -279,7 +444,9 @@ def fast_beam_search(
     lattice = decoding_streams.format_output(processed_lens.tolist())
     best_path = one_best_decoding(lattice)
     hyp_tokens = get_texts(best_path)
-    return hyp_tokens
+
+    for i in range(B):
+        streams[i].hyp = hyp_tokens[i]
 
 
 def decode_one_chunk(
@@ -305,8 +472,6 @@ def decode_one_chunk(
     features = []
     feature_lens = []
     states = []
-
-    rnnt_stream_list = []
     processed_lens = []
 
     for stream in decode_streams:
@@ -317,8 +482,6 @@ 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)
@@ -330,19 +493,13 @@ 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 = [
@@ -362,22 +519,31 @@ def decode_one_chunk(
     )
 
     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)]
 
@@ -385,8 +551,6 @@ 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)
 
@@ -469,13 +633,10 @@ 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]
@@ -489,24 +650,29 @@ 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}