import os
from dataclasses import dataclass
from typing import List, Tuple
import torch
import torchaudio
from huggingface_hub import hf_hub_download
from models import Model
from moshi.models import loaders
from tokenizers.processors import TemplateProcessing
from transformers import AutoTokenizer
from watermarking import load_watermarker, watermark
CSM_1B_HF_WATERMARK = list(map(int, os.getenv("WATERMARK_KEY").split(" ")))
@dataclass
class Segment:
speaker: int
text: str
# (num_samples,), sample_rate = 24_000
audio: torch.Tensor
def load_llama3_tokenizer():
"""
https://github.com/huggingface/transformers/issues/22794#issuecomment-2092623992
"""
tokenizer_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
bos = tokenizer.bos_token
eos = tokenizer.eos_token
tokenizer._tokenizer.post_processor = TemplateProcessing(
single=f"{bos}:0 $A:0 {eos}:0",
pair=f"{bos}:0 $A:0 {eos}:0 {bos}:1 $B:1 {eos}:1",
special_tokens=[(f"{bos}", tokenizer.bos_token_id), (f"{eos}", tokenizer.eos_token_id)],
)
return tokenizer
class Generator:
def __init__(
self,
model: Model,
):
self._model = model
self._model.setup_caches(1)
self._text_tokenizer = load_llama3_tokenizer()
device = next(model.parameters()).device
mimi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MIMI_NAME)
mimi = loaders.get_mimi(mimi_weight, device=device)
mimi.set_num_codebooks(32)
self._audio_tokenizer = mimi
self._watermarker = load_watermarker(device=device)
self.sample_rate = mimi.sample_rate
self.device = device
def _tokenize_text_segment(self, text: str, speaker: int) -> Tuple[torch.Tensor, torch.Tensor]:
frame_tokens = []
frame_masks = []
text_tokens = self._text_tokenizer.encode(f"[{speaker}]{text}")
text_frame = torch.zeros(len(text_tokens), 33).long()
text_frame_mask = torch.zeros(len(text_tokens), 33).bool()
text_frame[:, -1] = torch.tensor(text_tokens)
text_frame_mask[:, -1] = True
frame_tokens.append(text_frame.to(self.device))
frame_masks.append(text_frame_mask.to(self.device))
return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)
def _tokenize_audio(self, audio: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
frame_tokens = []
frame_masks = []
# (K, T)
audio = audio.to(self.device)
audio_tokens = self._audio_tokenizer.encode(audio.unsqueeze(0).unsqueeze(0))[0]
# add EOS frame
eos_frame = torch.zeros(audio_tokens.size(0), 1).to(self.device)
audio_tokens = torch.cat([audio_tokens, eos_frame], dim=1)
audio_frame = torch.zeros(audio_tokens.size(1), 33).long().to(self.device)
audio_frame_mask = torch.zeros(audio_tokens.size(1), 33).bool().to(self.device)
audio_frame[:, :-1] = audio_tokens.transpose(0, 1)
audio_frame_mask[:, :-1] = True
frame_tokens.append(audio_frame)
frame_masks.append(audio_frame_mask)
return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0)
def _tokenize_segment(self, segment: Segment) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Returns:
(seq_len, 33), (seq_len, 33)
"""
text_tokens, text_masks = self._tokenize_text_segment(segment.text, segment.speaker)
audio_tokens, audio_masks = self._tokenize_audio(segment.audio)
return torch.cat([text_tokens, audio_tokens], dim=0), torch.cat([text_masks, audio_masks], dim=0)
@torch.inference_mode()
def generate(
self,
text: str,
speaker: int,
context: List[Segment],
max_audio_length_ms: float = 90_000,
temperature: float = 0.9,
topk: int = 50,
) -> torch.Tensor:
self._model.reset_caches()
max_audio_frames = int(max_audio_length_ms / 80)
tokens, tokens_mask = [], []
for segment in context:
segment_tokens, segment_tokens_mask = self._tokenize_segment(segment)
tokens.append(segment_tokens)
tokens_mask.append(segment_tokens_mask)
gen_segment_tokens, gen_segment_tokens_mask = self._tokenize_text_segment(text, speaker)
tokens.append(gen_segment_tokens)
tokens_mask.append(gen_segment_tokens_mask)
prompt_tokens = torch.cat(tokens, dim=0).long().to(self.device)
prompt_tokens_mask = torch.cat(tokens_mask, dim=0).bool().to(self.device)
samples = []
curr_tokens = prompt_tokens.unsqueeze(0)
curr_tokens_mask = prompt_tokens_mask.unsqueeze(0)
curr_pos = torch.arange(0, prompt_tokens.size(0)).unsqueeze(0).long().to(self.device)
max_seq_len = 2048 - max_audio_frames
if curr_tokens.size(1) >= max_seq_len:
raise ValueError(f"Inputs too long, must be below max_seq_len - max_audio_frames: {max_seq_len}")
for _ in range(max_audio_frames):
sample = self._model.generate_frame(curr_tokens, curr_tokens_mask, curr_pos, temperature, topk)
if torch.all(sample == 0):
break # eos
samples.append(sample)
curr_tokens = torch.cat([sample, torch.zeros(1, 1).long().to(self.device)], dim=1).unsqueeze(1)
curr_tokens_mask = torch.cat(
[torch.ones_like(sample).bool(), torch.zeros(1, 1).bool().to(self.device)], dim=1
).unsqueeze(1)
curr_pos = curr_pos[:, -1:] + 1
audio = self._audio_tokenizer.decode(torch.stack(samples).permute(1, 2, 0)).squeeze(0).squeeze(0)
# This applies an imperceptible watermark to identify audio as AI-generated.
# Watermarking ensures transparency, dissuades misuse, and enables traceability.
# Please be a responsible AI citizen and keep the watermarking in place.
# If using CSM 1B in another application, use your own private key and keep it secret.
audio, wm_sample_rate = watermark(self._watermarker, audio, self.sample_rate, CSM_1B_HF_WATERMARK)
audio = torchaudio.functional.resample(audio, orig_freq=wm_sample_rate, new_freq=self.sample_rate)
return audio
def load_csm_1b(device: str = "cuda") -> Generator:
model = Model.from_pretrained("sesame/csm-1b")
model.to(device=device, dtype=torch.bfloat16)
generator = Generator(model)
return generator