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import re
import torch
import numpy as np
from transformers import MimiModel, GenerationConfig
from transformers import Pipeline, LogitsProcessor
class AlternatingCodebooksLogitsProcessor(LogitsProcessor):
def __init__(self, input_start_len: int, codebook_size: int, num_codebooks: int, offset: int, stop_token: int):
self.input_start_len = input_start_len
self.codebook_size = codebook_size
self.num_codebooks = num_codebooks
self.offset = offset
self.stop_token = stop_token
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
curr_len = input_ids.shape[-1]
codebook_idx = ((curr_len - self.input_start_len) % self.num_codebooks)
scores_processed = scores.clone()
scores_processed[:, : self.offset + codebook_idx * self.codebook_size] = -float("inf")
scores_processed[:, self.offset + (codebook_idx+1) * self.codebook_size :] = -float("inf")
scores_processed[:, self.stop_token] = scores[:, self.stop_token]
return scores_processed
class IndriTTSPipeline(Pipeline):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.audio_tokenizer = MimiModel.from_pretrained('kyutai/mimi').to(device=self.device)
# TODO: Ideally all of this should come from model config
self.convert_token = self.tokenizer.encode('[convert]')
self.stop_token = self.tokenizer.encode('[stop]')
self.text_modality_token = self.tokenizer.encode('[text]')
self.acoustic_modality_token = self.tokenizer.encode('[mimi]')
self.num_codebooks = 8
self.audio_offset = 50257
self.model.stop_token = self.stop_token
self.model.generation_config = GenerationConfig(
eos_token_id=self.stop_token,
max_length=kwargs.get('max_length', 1024),
temperature=kwargs.get('temperature', 0.5),
top_k=kwargs.get('top_k', 15),
do_sample=kwargs.get('do_sample', True)
)
def _sanitize_parameters(self, **kwargs):
speaker = kwargs.get('speaker', '[spkr_unk]')
preprocess_kwargs = {
'speaker': speaker
}
return preprocess_kwargs, {}, {}
def _prepare_tts_tokens(self, text_tokens, speaker):
input_tokens = np.hstack([
self.text_modality_token,
text_tokens,
self.convert_token,
self.acoustic_modality_token,
self.tokenizer.encode(speaker)
])
return input_tokens.tolist()
def _sanitize_text(self, text):
text = text.lower()
text = re.sub(r'\n+', ' ', text)
text = re.sub(r'[ \t]+', ' ', text)
text = re.sub(r'([,\.?])+', r'\1', text)
return text.strip()
def _deserialize_tokens(self, tokens, num_codebooks):
cb = [tokens[i::num_codebooks] for i in range(num_codebooks)]
min_shape = min([c.shape for c in cb])[0]
acoustic_tokens = torch.vstack([c[:min_shape] - 2048*i for i, c in enumerate(cb)])
return acoustic_tokens
# TODO: Use this to support batching
def _prepare_mimi_batch(self, tokens, attention_mask):
max_len = max(token.size(1) for token in tokens)
padded_tokens = []
padded_masks = []
for token, mask in zip(tokens, attention_masks):
pad_len = max_len - token.size(1)
padded_token = F.pad(token, (0, pad_len, 0, 0), value=0)
padded_mask = F.pad(mask, (0, pad_len, 0, 0), value=0)
padded_tokens.append(padded_token)
padded_masks.append(padded_mask)
stacked_tokens = torch.stack(padded_tokens, dim=0)
stacked_masks = torch.stack(padded_masks, dim=0)
return stacked_tokens, stacked_masks
def preprocess(self, inputs, speaker):
input_text = self._sanitize_text(inputs)
input_tokens = self.tokenizer.encode(input_text)
task_tokens = self._prepare_tts_tokens(input_tokens, speaker)
task_tokens = torch.tensor(task_tokens).unsqueeze(0)
return {'input_ids': task_tokens, 'attention_mask': torch.ones_like(task_tokens)}
def _forward(self, model_inputs, **forward_args):
logits_processor=[
AlternatingCodebooksLogitsProcessor(
input_start_len=model_inputs['input_ids'].shape[-1],
codebook_size=2048,
num_codebooks=self.num_codebooks,
offset=self.audio_offset,
stop_token=self.stop_token
)
]
outputs = self.model.generate(
model_inputs['input_ids'],
logits_processor=logits_processor
)
audio_tokens, attention_mask = [], []
for idx, inputs in enumerate(model_inputs['input_ids']):
truncated = outputs[idx, inputs.shape[-1]:]
end = torch.where(truncated == self.stop_token[0])[-1]
if end.shape[-1] > 0:
end = end[0]
else:
end = truncated.shape[-1]
truncated = truncated[:end]
truncated -= self.audio_offset
truncated = self._deserialize_tokens(torch.tensor(truncated), self.num_codebooks)
audio_tokens.append(truncated)
attention_mask.append(torch.ones_like(truncated))
audio_tokens = torch.vstack(audio_tokens).unsqueeze(0)
attention_mask = torch.vstack(attention_mask).unsqueeze(0)
audio = self.audio_tokenizer.decode(audio_tokens).audio_values
return {
'audio_tokens': audio_tokens, # (B, num_codebooks, num_samples)
'audio': audio # (B, 1, num_audio_samples)
}
def postprocess(self, model_outputs):
return model_outputs
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