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revert changes in decode.py and utils.py
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@ -26,7 +26,7 @@ import torch
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from icefall.context_graph import ContextGraph, ContextState
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from icefall.lm_wrapper import LmScorer
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from icefall.ngram_lm import NgramLm, NgramLmStateCost
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from icefall.utils import DecodingResults, add_eos, add_sos, get_texts
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from icefall.utils import add_eos, add_sos, get_texts
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DEFAULT_LM_SCALE = [
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0.01,
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@ -1485,52 +1485,25 @@ def ctc_greedy_search(
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ctc_output: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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blank_id: int = 0,
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return_timestamps: bool = False,
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) -> Union[List[List[int]], DecodingResults]:
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) -> List[List[int]]:
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"""CTC greedy search.
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Args:
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ctc_output: (batch, seq_len, vocab_size)
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encoder_out_lens: (batch,)
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Returns:
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Union[List[List[int]], DecodingResults]: greedy search result
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List[List[int]]: greedy search result
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"""
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batch = ctc_output.shape[0]
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index = ctc_output.argmax(dim=-1) # (batch, seq_len)
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hyps = [[] for _ in range(batch)]
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# timestamps[n][i] is the frame index after subsampling
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# on which hyp[n][i] is decoded
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timestamps = [[] for _ in range(batch)]
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# scores[n][i] is the logits on which hyp[n][i] is decoded
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scores = [[] for _ in range(batch)]
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for i in range(batch):
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cur_pos = -1
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last = -1
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next_other = torch.where(index[i,0 : encoder_out_lens[i]] != last)[0]
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flag = False # whether decoding words
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while next_other.size(0) > 0:
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cur_pos = cur_pos + 1 + next_other[0].item()
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last = index[i,cur_pos].item()
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if flag: # last word decoding finished
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timestamps[i][-1] = timestamps[i][-1] + (cur_pos - 1, )
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if last != blank_id:
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hyps[i].append(last)
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timestamps[i].append((cur_pos,))
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scores[i].append(ctc_output[i, cur_pos, last].item())
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flag = True # Decoding words
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else:
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flag = False
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next_other = torch.where(index[i,cur_pos + 1 : encoder_out_lens[i]] != last)[0]
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if flag:
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timestamps[i][-1] = timestamps[i][-1] + (encoder_out_lens[i].item() - 1, )
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if not return_timestamps:
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return hyps
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else:
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return DecodingResults(hyps = hyps, timestamps = timestamps, scores = scores)
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hyps = [
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torch.unique_consecutive(index[i, : encoder_out_lens[i]]) for i in range(batch)
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]
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hyps = [h[h != blank_id].tolist() for h in hyps]
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return hyps
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@dataclass
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class Hypothesis:
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@ -360,7 +360,7 @@ class KeywordResult:
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class DecodingResults:
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# timestamps[i][k] contains the frame number on which tokens[i][k]
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# is decoded
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timestamps: List[List[Union[int, Tuple[int, int]]]]
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timestamps: List[List[int]]
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# hyps[i] is the recognition results, i.e., word IDs or token IDs
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# for the i-th utterance with fast_beam_search_nbest_LG.
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@ -583,40 +583,6 @@ def store_transcripts_and_timestamps(
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s = "[" + ", ".join(["%0.3f" % i for i in time_hyp]) + "]"
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print(f"{cut_id}:\ttimestamp_hyp={s}", file=f)
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def store_transcripts_and_timestamps_withoutref(
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filename: Pathlike,
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texts: Iterable[Tuple[str, List[str], List[str], List[Tuple[float]]]],
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) -> None:
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"""Save predicted results and reference transcripts as well as their timestamps
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to a file.
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Args:
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filename:
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File to save the results to.
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texts:
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An iterable of tuples. The first element is the cur_id, the second is
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the reference transcript and the third element is the predicted result.
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Returns:
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Return None.
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"""
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with open(filename, "w", encoding="utf8") as f:
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for cut_id, ref, hyp, time_hyp in texts:
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print(f"{cut_id}:\tref={ref}", file=f)
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print(f"{cut_id}:\thyp={hyp}", file=f)
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if len(time_hyp) > 0:
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if isinstance(time_hyp[0], tuple):
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# each element is <start, end> pair
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s = (
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"["
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+ ", ".join(["(%0.3f, %.03f)" % (i, j) for (i, j) in time_hyp])
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+ "]"
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)
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else:
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# each element is a float number
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s = "[" + ", ".join(["%0.3f" % i for i in time_hyp]) + "]"
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print(f"{cut_id}:\ttimestamp_hyp={s}", file=f)
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def write_error_stats(
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f: TextIO,
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@ -1873,30 +1839,6 @@ def convert_timestamp(
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return time
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def convert_timestamp_duration(
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frames: List[Tuple[int, int]],
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subsampling_factor: int,
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frame_shift_ms: float = 10,
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) -> List[Tuple[float, float]]:
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"""Convert frame numbers to time (in seconds) given subsampling factor
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and frame shift (in milliseconds).
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Args:
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frames:
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A list of frame numbers after subsampling.
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subsampling_factor:
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The subsampling factor of the model.
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frame_shift_ms:
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Frame shift in milliseconds between two contiguous frames.
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Return:
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Return the time in seconds corresponding to each given frame.
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"""
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frame_shift = frame_shift_ms / 1000.0
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time = []
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for f_start, f_end in frames:
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time.append((round(f_start * subsampling_factor * frame_shift, ndigits=3), round(f_end * subsampling_factor * frame_shift, ndigits=3)))
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return time
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def parse_timestamp(tokens: List[str], timestamp: List[float]) -> List[float]:
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"""
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@ -1982,43 +1924,6 @@ def parse_hyp_and_timestamp(
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return hyps, timestamps
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def parse_hyp_and_timestamp_ch(
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res: DecodingResults,
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subsampling_factor: int,
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word_table: k2.SymbolTable,
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frame_shift_ms: float = 10,
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) -> Tuple[List[List[str]], List[List[float]]]:
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"""Parse hypothesis and timestamps of Chinese characters.
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Args:
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res:
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A DecodingResults object.
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subsampling_factor:
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The integer subsampling factor.
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word_table:
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The word symbol table.
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frame_shift_ms:
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The float frame shift used for feature extraction.
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Returns:
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Return a list of hypothesis and timestamp.
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"""
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hyps = []
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timestamps = []
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N = len(res.hyps)
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assert len(res.timestamps) == N, (len(res.timestamps), N)
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assert word_table is not None
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for i in range(N):
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time = convert_timestamp_duration(res.timestamps[i], subsampling_factor, frame_shift_ms)
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words_decoded = [word_table[idx] for idx in res.hyps[i]]
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hyps.append(words_decoded)
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timestamps.append(time)
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assert len(time) == len(words_decoded), (len(time), len(words_decoded))
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return hyps, timestamps
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# `is_module_available` is copied from
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# https://github.com/pytorch/audio/blob/6bad3a66a7a1c7cc05755e9ee5931b7391d2b94c/torchaudio/_internal/module_utils.py#L9
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