unified_voice2

codes/models/gpt_voice/unified_voice2.py

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+import functools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Model, GPT2Config
+
+from models.arch_util import AttentionBlock
+from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel
+from models.gpt_voice.gpt_asr_hf2 import ResBlock
+from models.gpt_voice.transformer_builders import build_hf_gpt_transformer
+from models.tacotron2.text import symbols
+from trainer.networks import register_model
+from utils.util import opt_get
+
+
+class ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=4,
+                 do_checkpointing=False):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+        self.do_checkpointing = do_checkpointing
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h)
+        return h[:, :, 0]
+
+
+class MelEncoder(nn.Module):
+    def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
+        super().__init__()
+        self.channels = channels
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
+                                     nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//16, channels//2),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//8, channels),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
+                                     )
+        self.reduction = 4
+
+
+    def forward(self, x):
+        for e in self.encoder:
+            x = e(x)
+        return x.permute(0,2,1)
+
+
+class UnifiedVoice(nn.Module):
+    def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
+                 max_conditioning_length=60, shuffle_conditioning=True, mel_length_compression=1024, number_text_tokens=256,
+                 start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
+                 stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
+                 checkpointing=True):
+        """
+        Args:
+            layers: Number of layers in transformer stack.
+            model_dim: Operating dimensions of the transformer
+            heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
+            max_text_tokens: Maximum number of text tokens that will be encountered by model.
+            max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
+            max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
+            max_conditioning_length: Maximum length of conditioning input. Only needed if shuffle_conditioning=True
+            shuffle_conditioning: Whether or not the conditioning inputs will be shuffled across the sequence dimension. Useful if you want to provide the same input as conditioning and mel_codes.
+            mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
+            number_text_tokens:
+            start_text_token:
+            stop_text_token:
+            number_mel_codes:
+            start_mel_token:
+            stop_mel_token:
+            train_solo_embeddings:
+            use_mel_codes_as_input:
+            checkpointing:
+        """
+        super().__init__()
+
+        self.number_text_tokens = number_text_tokens
+        self.start_text_token = start_text_token
+        self.stop_text_token = stop_text_token
+        self.number_mel_codes = number_mel_codes
+        self.start_mel_token = start_mel_token
+        self.stop_mel_token = stop_mel_token
+        self.shuffle_conditioning = shuffle_conditioning
+        self.layers = layers
+        self.heads = heads
+        self.max_mel_tokens = max_mel_tokens
+        self.max_text_tokens = max_text_tokens
+        self.model_dim = model_dim
+        self.max_conditioning_inputs = max_conditioning_inputs
+        self.mel_length_compression = mel_length_compression
+        self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
+        self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
+        self.text_pos_embedding = nn.Embedding(self.max_text_tokens + 2, model_dim)
+        if use_mel_codes_as_input:
+            self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+        else:
+            self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
+        self.mel_pos_embedding = nn.Embedding(self.max_mel_tokens + 2, model_dim)
+        self.seq_length = 4+max_text_tokens+self.max_mel_tokens+self.max_conditioning_inputs
+        self.gpt = build_hf_gpt_transformer(layers, model_dim, heads, number_mel_codes, self.seq_length, checkpointing)
+        if train_solo_embeddings:
+            self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+            self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+        else:
+            self.mel_solo_embedding = 0
+            self.text_solo_embedding = 0
+
+        self.final_norm = nn.LayerNorm(model_dim)
+        self.text_head = nn.Linear(model_dim, self.number_text_tokens)
+        self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
+        self.max_conditioning_length = max_conditioning_length
+
+        # Initialize the embeddings per the GPT-2 scheme
+        embeddings = [self.text_embedding, self.text_pos_embedding, self.mel_pos_embedding]
+        if use_mel_codes_as_input:
+            embeddings.append(self.mel_embedding)
+        for module in:
+            module.weight.data.normal_(mean=0.0, std=.02)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
+        inp = F.pad(input, (1,0), value=start_token)
+        tar = F.pad(input, (0,1), value=stop_token)
+        return inp, tar
+
+    def set_mel_padding(self, mel_input_tokens, wav_lengths):
+        """
+        Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
+        that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
+        preformatting to create a working TTS model.
+        """
+        # Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
+        mel_lengths = wav_lengths // self.mel_length_compression
+        for b in range(len(mel_lengths)):
+            actual_end = mel_lengths[b] + 1  # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
+            if actual_end < mel_input_tokens.shape[-1]:
+                mel_input_tokens[b, actual_end:] = self.stop_mel_token
+        return mel_input_tokens
+
+    def randomly_permute_conditioning_input(self, speech_conditioning_input):
+        """
+        Randomly permute the conditioning spectrogram, to destroy any structure present. Note that since the
+        conditioning input is derived from a discrete spectrogram, it does actually retain structure, but only a little
+        bit (actually: exactly how much we want; enough to discriminate different vocal qualities, but nothing about
+        what is being said).
+        """
+        cond_input = speech_conditioning_input[:,:,torch.randperm(speech_conditioning_input.shape[-1])]
+        if cond_input.shape[-1] > self.max_conditioning_length:
+            cond_input = cond_input[:,:,:self.max_conditioning_length]
+        return cond_input
+
+    def get_logits(self, speech_conditioning_input, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
+        if second_inputs is not None:
+            emb = torch.cat([speech_conditioning_input, first_inputs, second_inputs], dim=1)
+        else:
+            emb = torch.cat([speech_conditioning_input, first_inputs], dim=1)
+
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        if get_attns:
+            return gpt_out.attentions
+
+        enc = gpt_out.last_hidden_state[:, 1:]  # The first logit is tied to the speech_conditioning_input
+        enc = self.final_norm(enc)
+        first_logits = enc[:, :first_inputs.shape[1]]
+        first_logits = first_head(first_logits)
+        first_logits = first_logits.permute(0,2,1)
+        if second_inputs is not None:
+            second_logits = enc[:, -second_inputs.shape[1]:]
+            second_logits = second_head(second_logits)
+            second_logits = second_logits.permute(0,2,1)
+            return first_logits, second_logits
+        else:
+            return first_logits
+
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False):
+        """
+        Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
+        (actuated by `text_first`).
+
+        speech_conditioning_input: MEL float tensor, (b,80,s)
+        text_inputs: long tensor, (b,t)
+        text_lengths: long tensor, (b,)
+        mel_inputs:  long tensor, (b,m)
+        wav_lengths: long tensor, (b,)
+        raw_mels: MEL float tensor (b,80,s)
+        """
+        assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
+        assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
+
+        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
+        # chopping the inputs by the maximum actual length.
+        max_text_len = text_lengths.max()
+        text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
+        max_mel_len = wav_lengths.max() // self.mel_length_compression
+        mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
+        if raw_mels is not None:
+            raw_mels = raw_mels[:, :, :max_mel_len*4]
+        mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
+
+        if self.shuffle_conditioning:
+            speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
+        speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
+
+        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
+        mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
+        if raw_mels is not None:
+            mel_inp = F.pad(raw_mels, (0, 8))
+        else:
+            mel_inp = mel_codes
+        mel_emb = self.mel_embedding(mel_inp)
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
+        if text_first:
+            text_logits, mel_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
+        else:
+            mel_logits, text_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
+
+        if return_attentions:
+            return mel_logits
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        return loss_text.mean(), loss_mel.mean(), mel_logits
+
+    def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
+        """
+        Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
+        model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
+        """
+        assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
+
+        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
+        # chopping the inputs by the maximum actual length.
+        max_text_len = text_lengths.max()
+        text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
+
+        if self.shuffle_conditioning:
+            speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
+        speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
+
+        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device)) + self.text_solo_embedding
+        text_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head)
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        return loss_text.mean()
+
+    def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None):
+        """
+        Performs autoregressive modeling on only speech data.
+        """
+        assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
+
+        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
+        # chopping the inputs by the maximum actual length.
+        max_mel_len = wav_lengths.max() // self.mel_length_compression
+        mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
+        mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
+        if raw_mels is not None:
+            raw_mels = raw_mels[:, :, :max_mel_len*4]
+
+        if self.shuffle_conditioning:
+            speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
+        speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
+
+        mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
+        if raw_mels is not None:
+            mel_inp = F.pad(raw_mels, (0, 4))
+        else:
+            mel_inp = mel_codes
+        mel_emb = self.mel_embedding(mel_inp)
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device)) + self.mel_solo_embedding
+        mel_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head)
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        return loss_mel.mean()
+
+    def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
+        if not hasattr(self, 'inference_model'):
+            # TODO: Decouple gpt_config from this inference model.
+            gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
+                                    n_positions=self.seq_length,
+                                    n_ctx=self.seq_length,
+                                    n_embd=self.model_dim,
+                                    n_layer=self.layers,
+                                    n_head=self.heads,
+                                    gradient_checkpointing=False,
+                                    use_cache=True)
+            self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head)
+
+        text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
+
+        if self.shuffle_conditioning:
+            # Randomly permute the conditioning spectrogram, to destroy any structure present.
+            speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
+        cond = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
+
+        emb = torch.cat([cond, text_emb], dim=1)
+        self.inference_model.store_mel_emb(emb)
+
+        fake_inputs = torch.full((emb.shape[0], emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device)
+        fake_inputs[:,-1] = self.start_mel_token
+
+        gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token,
+                                            max_length=self.seq_length, **hf_generate_kwargs)
+        return gen[:, fake_inputs.shape[1]:]
+
+
+@register_model
+def register_unified_voice2(opt_net, opt):
+    return UnifiedVoice(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True)
+    l = gpt(torch.randn(2, 80, 800),
+            torch.randint(high=len(symbols), size=(2,80)),
+            torch.tensor([32, 80]),
+            torch.randint(high=8192, size=(2,250)),
+            torch.tensor([150*256,195*256]))
+    gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))