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* Begin to add RNN-T training for librispeech. * Copy files from conformer_ctc. Will edit it. * Use conformer/transformer model as encoder. * Begin to add training script. * Add training code. * Remove long utterances to avoid OOM when a large max_duraiton is used. * Begin to add decoding script. * Add decoding script. * Minor fixes. * Add beam search. * Use LSTM layers for the encoder. Need more tunings. * Use stateless decoder. * Minor fixes to make it ready for merge. * Fix README. * Update RESULT.md to include RNN-T Conformer. * Minor fixes. * Fix tests. * Minor fixes. * Minor fixes. * Fix tests.
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List
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import torch
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from transducer.model import Transducer
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def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[str]:
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"""
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Args:
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model:
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An instance of `Transducer`.
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encoder_out:
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A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
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Returns:
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Return the decoded result.
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"""
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assert encoder_out.ndim == 3
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# support only batch_size == 1 for now
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assert encoder_out.size(0) == 1, encoder_out.size(0)
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blank_id = model.decoder.blank_id
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device = model.device
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sos = torch.tensor([blank_id], device=device).reshape(1, 1)
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decoder_out, (h, c) = model.decoder(sos)
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T = encoder_out.size(1)
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t = 0
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hyp = []
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max_u = 1000 # terminate after this number of steps
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u = 0
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while t < T and u < max_u:
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# fmt: off
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current_encoder_out = encoder_out[:, t:t+1, :]
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# fmt: on
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logits = model.joiner(current_encoder_out, decoder_out)
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log_prob = logits.log_softmax(dim=-1)
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# log_prob is (N, 1, 1)
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# TODO: Use logits.argmax()
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y = log_prob.argmax()
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if y != blank_id:
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hyp.append(y.item())
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y = y.reshape(1, 1)
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decoder_out, (h, c) = model.decoder(y, (h, c))
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u += 1
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else:
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t += 1
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id2word = {1: "YES", 2: "NO"}
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hyp = [id2word[i] for i in hyp]
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return hyp
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