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16a2748d6c
* Add PromptASR with BERT as text encoder * Support using word-list based content prompts for context biasing * Upload the pretrained models to huggingface * Add usage example
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang)
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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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import torch
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import torch.nn as nn
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from scaling import ScaledLinear
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class Joiner(nn.Module):
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def __init__(
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self,
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encoder_dim: int,
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decoder_dim: int,
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joiner_dim: int,
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vocab_size: int,
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context_dim: int = 512,
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context_injection: bool = False,
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):
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super().__init__()
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self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim, initial_scale=0.25)
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self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim, initial_scale=0.25)
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self.output_linear = nn.Linear(joiner_dim, vocab_size)
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if context_injection:
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self.context_proj = ScaledLinear(
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context_dim, joiner_dim, initial_scale=0.25
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)
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else:
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self.context_proj = None
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def forward(
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self,
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encoder_out: torch.Tensor,
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decoder_out: torch.Tensor,
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context: torch.Tensor = None,
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project_input: bool = True,
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) -> torch.Tensor:
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"""
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Args:
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encoder_out:
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Output from the encoder. Its shape is (N, T, s_range, C).
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decoder_out:
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Output from the decoder. Its shape is (N, T, s_range, C).
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context:
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An embedding vector representing the previous context information
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project_input:
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If true, apply input projections encoder_proj and decoder_proj.
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If this is false, it is the user's responsibility to do this
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manually.
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Returns:
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Return a tensor of shape (N, T, s_range, C).
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"""
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assert encoder_out.ndim == decoder_out.ndim == 4
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assert encoder_out.shape[:-1] == decoder_out.shape[:-1]
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if project_input:
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if context:
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logit = (
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self.encoder_proj(encoder_out)
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+ self.decoder_proj(decoder_out)
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+ self.context_proj(context)
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)
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else:
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logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
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else:
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if context is not None:
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logit = encoder_out + decoder_out + context.unsqueeze(1).unsqueeze(1)
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else:
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logit = encoder_out + decoder_out
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logit = self.output_linear(torch.tanh(logit))
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return logit
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