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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "moshi==0.2.11",
# "torch",
# "sphn",
# "sounddevice",
# ]
# ///
import argparse
from dataclasses import dataclass
import sys
import numpy as np
import queue
import sphn
import time
import torch
import typing as tp
from moshi.models.loaders import CheckpointInfo
from moshi.conditioners import dropout_all_conditions
from moshi.models.lm import LMGen
from moshi.models.tts import (
Entry,
DEFAULT_DSM_TTS_REPO,
DEFAULT_DSM_TTS_VOICE_REPO,
TTSModel,
ConditionAttributes,
script_to_entries,
)
def prepare_script(model: TTSModel, script: str, first_turn: bool) -> list[Entry]:
multi_speaker = first_turn and model.multi_speaker
return script_to_entries(
model.tokenizer,
model.machine.token_ids,
model.mimi.frame_rate,
[script],
multi_speaker=multi_speaker,
padding_between=1,
)
def _make_null(
all_attributes: tp.Sequence[ConditionAttributes],
) -> list[ConditionAttributes]:
# When using CFG, returns the null conditions.
return dropout_all_conditions(all_attributes)
@dataclass
class TTSGen:
tts_model: TTSModel
attributes: tp.Sequence[ConditionAttributes]
on_frame: tp.Optional[tp.Callable[[torch.Tensor], None]] = None
def __post_init__(self):
tts_model = self.tts_model
attributes = self.attributes
self.offset = 0
self.state = self.tts_model.machine.new_state([])
if tts_model.cfg_coef != 1.0:
if tts_model.valid_cfg_conditionings:
raise ValueError(
"This model does not support direct CFG, but was trained with "
"CFG distillation. Pass instead `cfg_coef` to `make_condition_attributes`."
)
nulled = _make_null(attributes)
attributes = list(attributes) + nulled
assert tts_model.lm.condition_provider is not None
prepared = tts_model.lm.condition_provider.prepare(attributes)
condition_tensors = tts_model.lm.condition_provider(prepared)
def _on_text_logits_hook(text_logits):
if tts_model.padding_bonus:
text_logits[..., tts_model.machine.token_ids.pad] += (
tts_model.padding_bonus
)
return text_logits
def _on_audio_hook(audio_tokens):
audio_offset = tts_model.lm.audio_offset
delays = tts_model.lm.delays
for q in range(audio_tokens.shape[1]):
delay = delays[q + audio_offset]
if self.offset < delay + tts_model.delay_steps:
audio_tokens[:, q] = tts_model.machine.token_ids.zero
def _on_text_hook(text_tokens):
tokens = text_tokens.tolist()
out_tokens = []
for token in tokens:
out_token, _ = tts_model.machine.process(self.offset, self.state, token)
out_tokens.append(out_token)
text_tokens[:] = torch.tensor(
out_tokens, dtype=torch.long, device=text_tokens.device
)
tts_model.lm.dep_q = tts_model.n_q
self.lm_gen = LMGen(
tts_model.lm,
temp=tts_model.temp,
temp_text=tts_model.temp,
cfg_coef=tts_model.cfg_coef,
condition_tensors=condition_tensors,
on_text_logits_hook=_on_text_logits_hook,
on_text_hook=_on_text_hook,
on_audio_hook=_on_audio_hook,
cfg_is_masked_until=None,
cfg_is_no_text=True,
)
self.lm_gen.streaming_forever(1)
def process_last(self):
while len(self.state.entries) > 0 or self.state.end_step is not None:
self._step()
additional_steps = (
self.tts_model.delay_steps + max(self.tts_model.lm.delays) + 8
)
for _ in range(additional_steps):
self._step()
def process(self):
while len(self.state.entries) > self.tts_model.machine.second_stream_ahead:
self._step()
def _step(self):
missing = self.tts_model.lm.n_q - self.tts_model.lm.dep_q
input_tokens = torch.full(
(1, missing, 1),
self.tts_model.machine.token_ids.zero,
dtype=torch.long,
device=self.tts_model.lm.device,
)
frame = self.lm_gen.step(input_tokens)
self.offset += 1
if frame is not None:
if self.on_frame is not None:
self.on_frame(frame)
def append_entry(self, entry):
self.state.entries.append(entry)
@torch.no_grad()
def main():
parser = argparse.ArgumentParser(
description="Run Kyutai TTS using the PyTorch implementation"
)
parser.add_argument(
"out", type=str, help="Output file to generate, use - for playing the audio"
)
parser.add_argument(
"--hf-repo",
type=str,
default=DEFAULT_DSM_TTS_REPO,
help="HF repo in which to look for the pretrained models.",
)
parser.add_argument(
"--voice-repo",
default=DEFAULT_DSM_TTS_VOICE_REPO,
help="HF repo in which to look for pre-computed voice embeddings.",
)
parser.add_argument(
"--voice",
default="expresso/ex03-ex01_happy_001_channel1_334s.wav",
help="The voice to use, relative to the voice repo root. "
f"See {DEFAULT_DSM_TTS_VOICE_REPO}",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
help="Device on which to run, defaults to 'cuda'.",
)
args = parser.parse_args()
print("Loading model...")
checkpoint_info = CheckpointInfo.from_hf_repo(args.hf_repo)
tts_model = TTSModel.from_checkpoint_info(
checkpoint_info, n_q=32, temp=0.6, device=args.device
)
if args.voice.endswith(".safetensors"):
voice_path = args.voice
else:
voice_path = tts_model.get_voice_path(args.voice)
# CFG coef goes here because the model was trained with CFG distillation,
# so it's not _actually_ doing CFG at inference time.
# Also, if you are generating a dialog, you should have two voices in the list.
condition_attributes = tts_model.make_condition_attributes(
[voice_path], cfg_coef=2.0
)
if sys.stdin.isatty(): # Interactive
print("Enter text to synthesize (Ctrl+D to end input):")
if args.out == "-":
# Stream the audio to the speakers using sounddevice.
import sounddevice as sd
pcms = queue.Queue()
def _on_frame(frame):
if (frame != -1).all():
pcm = tts_model.mimi.decode(frame[:, 1:, :]).cpu().numpy()
pcms.put_nowait(np.clip(pcm[0, 0], -1, 1))
def audio_callback(outdata, _a, _b, _c):
try:
pcm_data = pcms.get(block=False)
outdata[:, 0] = pcm_data
except queue.Empty:
outdata[:] = 0
gen = TTSGen(tts_model, [condition_attributes], on_frame=_on_frame)
with sd.OutputStream(
samplerate=tts_model.mimi.sample_rate,
blocksize=1920,
channels=1,
callback=audio_callback,
) and tts_model.mimi.streaming(1):
first_turn = True
for line in sys.stdin:
entries = prepare_script(tts_model, line.strip(), first_turn=first_turn)
first_turn = False
for entry in entries:
gen.append_entry(entry)
gen.process()
gen.process_last()
while True:
if pcms.qsize() == 0:
break
time.sleep(1)
else:
pcms = []
def _on_frame(frame: torch.Tensor):
if (frame != -1).all():
pcm = tts_model.mimi.decode(frame[:, 1:, :]).cpu().numpy()
pcms.append(np.clip(pcm[0, 0]))
gen = TTSGen(tts_model, [condition_attributes], on_frame=_on_frame)
with tts_model.mimi.streaming(1):
first_turn = True
for line in sys.stdin:
entries = prepare_script(tts_model, line.strip(), first_turn=first_turn)
first_turn = False
for entry in entries:
gen.append_entry(entry)
gen.process()
gen.process_last()
pcm = np.concatenate(pcms, axis=-1)
sphn.write_wav(args.out, pcm, tts_model.mimi.sample_rate)
if __name__ == "__main__":
main()
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