forked from mrq/DL-Art-School
better bounds
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@ -288,6 +288,7 @@ class GptAsrHf2(nn.Module):
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mel_len = 0
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else:
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mel_emb = self.mel_encoder(mel_inputs)
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assert mel_emb.shape[1] <= self.max_mel_frames
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mel_emb = mel_emb.permute(0,2,1).contiguous()
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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emb = torch.cat([mel_emb, text_emb], dim=1)
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@ -302,6 +303,9 @@ class GptAsrHf2(nn.Module):
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return text_logits
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def forward(self, mel_inputs, text_inputs, return_attentions=False):
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assert text_inputs.shape[1] <= self.max_symbols_per_phrase
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assert text_inputs.max() <= self.number_text_tokens
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_token, self.stop_token)
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text_emb = self.gpt.get_input_embeddings()(text_inputs) + \
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self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
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@ -313,9 +317,9 @@ class GptAsrHf2(nn.Module):
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return loss_text.mean(), text_logits
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def text_only(self, text_inputs):
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if text_inputs.shape[1] > self.max_symbols_per_phrase:
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print(f"Embedding error, provided text_inputs with shape {text_inputs.shape}, but max is {self.max_symbols_per_phrase}. Automatically correcting by truncating symbols.")
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text_inputs = text_inputs[:, :self.max_symbols_per_phrase]
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assert text_inputs.shape[1] <= self.max_symbols_per_phrase
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assert text_inputs.max() <= self.number_text_tokens
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_token, self.stop_token)
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text_emb = self.gpt.get_input_embeddings()(text_inputs) + \
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self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device)) + \
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