From e1bdd3f7c749386e33eb10e74078b281b6dfd349 Mon Sep 17 00:00:00 2001 From: James Betker Date: Fri, 13 Aug 2021 22:47:00 -0600 Subject: [PATCH] Fix gpt_asr bug. Initial implementation of beam search --- codes/models/gpt_voice/gpt_asr.py | 71 +++++++++++++++++++++++++++++-- 1 file changed, 67 insertions(+), 4 deletions(-) diff --git a/codes/models/gpt_voice/gpt_asr.py b/codes/models/gpt_voice/gpt_asr.py index da606ee6..af4995a7 100644 --- a/codes/models/gpt_voice/gpt_asr.py +++ b/codes/models/gpt_voice/gpt_asr.py @@ -52,7 +52,7 @@ class GptAsr(nn.Module): MAX_SYMBOLS_PER_PHRASE = 200 MAX_MEL_FRAMES = 1000 // 4 NUMBER_SYMBOLS = len(symbols) - NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS + NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1 def __init__(self, layers=8, model_dim=512, heads=8): super().__init__() @@ -61,15 +61,17 @@ class GptAsr(nn.Module): self.max_mel_frames = self.MAX_MEL_FRAMES self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim) self.mel_encoder = MelEncoder(model_dim) - self.text_pos_embedding = nn.Embedding(self.MAX_SYMBOLS_PER_PHRASE, model_dim) + self.text_pos_embedding = nn.Embedding(self.MAX_SYMBOLS_PER_PHRASE+1, model_dim) self.mel_pos_embedding = nn.Embedding(self.MAX_MEL_FRAMES, model_dim) - self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=1+self.MAX_SYMBOLS_PER_PHRASE+self.MAX_MEL_FRAMES, heads=heads, + self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=2+self.MAX_SYMBOLS_PER_PHRASE+self.MAX_MEL_FRAMES, heads=heads, attn_dropout=.1, ff_dropout=.1, non_causal_sequence_partition=self.MAX_MEL_FRAMES) self.final_norm = nn.LayerNorm(model_dim) self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS) def forward(self, mel_inputs, text_targets): + # Pad front and back. Pad at front is the "START" token. + text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS) text_targets = F.pad(text_targets, (0, self.MAX_SYMBOLS_PER_PHRASE-text_targets.shape[1])) text_emb = self.text_embedding(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device)) @@ -85,10 +87,71 @@ class GptAsr(nn.Module): text_logits = self.final_norm(enc[:, self.MAX_MEL_FRAMES:]) text_logits = self.text_head(text_logits) text_logits = text_logits.permute(0,2,1) - loss_text = F.cross_entropy(text_logits[:,:,1:], text_targets[:,:-1].long()) + loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long()) return loss_text.mean() + def inference_beam_topk(self, mel): + def topk_sampler(distribution, k): + return torch.topk(distribution, k=k, dim=-1) + return self.inference_beam(mel, topk_sampler) + + def inference_beam_sampled(self, mel): + def multinomial_sampler(distribution, k): + indices = torch.multinomial(distribution, num_samples=k, replacement=False) + values = torch.gather(distribution, dim=1, index=indices) + class container: + def __init__(self, i, v): + self.indices = i + self.values = v + return container(indices, values) + return self.inference_beam(mel, multinomial_sampler) + + def inference_beam(self, mel_inputs, sampler_fn): + beam_width = 16 + temperature = .8 + + b, _, s = mel_inputs.shape + assert b == 1 # Beam search only works on batches of one. + mel_emb = self.mel_encoder(mel_inputs) + mel_emb = F.pad(mel_emb, (0, self.MAX_MEL_FRAMES-mel_emb.shape[-1])) + mel_emb = mel_emb.permute(0,2,1).contiguous() + mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device)) + + text_seq = torch.full((b,1), fill_value=self.NUMBER_SYMBOLS, device=mel_emb.device) + probabilities = torch.ones((b,), device=mel_emb.device) + while len(text_seq) < self.max_mel_frames: + text_emb = self.text_embedding(text_seq) + text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=mel_emb.device)) + if text_emb.shape[0] != mel_emb.shape[0]: + mel_emb = mel_emb.repeat(text_emb.shape[0], 1, 1) + emb = torch.cat([text_emb, mel_emb], dim=1) + enc = self.gpt(emb) + mel_logits = self.final_norm(enc[:, text_emb.shape[1]:]) + mel_logits = self.mel_head(mel_logits) + topk = sampler_fn(F.softmax(temperature * mel_logits[:, -1], dim=-1), k=beam_width) + probabilities = (probabilities.repeat_interleave(beam_width, dim=0) * topk.values.flatten()) + probabilities, sort_indices = torch.sort(probabilities, descending=True) + probabilities = probabilities[:beam_width] + + text_seq = text_seq.repeat_interleave(beam_width, dim=0) + codes = topk.indices.flatten() + text_seq = torch.cat([text_seq, codes.unsqueeze(1)], dim=1) + text_seq = text_seq[sort_indices] + text_seq = text_seq[:beam_width] + + if torch.all(torch.any(text_seq == self.MEL_STOP_TOKEN, dim=1)): + break + + if text_seq.shape[1] >= self.max_mel_frames: + print("Warning! Encountered frame limit before a stop token. Output is likely wrong.") + + # Format mel_seq so that the DVAE can actually use it (it is a two-tiered DVAE) + text_seq = text_seq[0, 1:-1].unsqueeze(0) # Pick most likely outcome, remove first and last tokens, which were artificially added for GPT + text_seq = text_seq * (text_seq < 512) # The DVAE doesn't understand BOS/EOS/PAD tokens. + + return text_seq + @register_model def register_gpt_asr(opt_net, opt):