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from collections import namedtuple
import soundfile as sf
import torch
from loguru import logger
from melo.api import TTS as MeloTTS
from parler_tts import ParlerTTSForConditionalGeneration
from replicate import Client
from transformers import AutoTokenizer
from kitt.skills.common import config
replicate = Client(api_token=config.REPLICATE_API_KEY)
Voice = namedtuple("voice", ["name", "neutral", "angry", "speed"])
voices_replicate = [
Voice(
"Fast",
neutral="empty",
angry=None,
speed=1.0,
),
Voice(
"Attenborough",
neutral="https://zebel.ams3.digitaloceanspaces.com/xtts/short/attenborough-neutral.wav",
angry=None,
speed=1.2,
),
Voice(
"Rick",
neutral="https://zebel.ams3.digitaloceanspaces.com/xtts/short/rick-neutral.wav",
angry=None,
speed=1.2,
),
Voice(
"Freeman",
neutral="https://zebel.ams3.digitaloceanspaces.com/xtts/short/freeman-neutral.wav",
angry="https://zebel.ams3.digitaloceanspaces.com/xtts/short/freeman-angry.wav",
speed=1.1,
),
Voice(
"Walken",
neutral="https://zebel.ams3.digitaloceanspaces.com/xtts/short/walken-neutral.wav",
angry=None,
speed=1.1,
),
Voice(
"Darth Wader",
neutral="https://zebel.ams3.digitaloceanspaces.com/xtts/short/darth-neutral.wav",
angry=None,
speed=1.15,
),
]
def voice_from_text(voice, voices):
for v in voices:
if voice == f"{v.name} - Neutral":
return v.neutral
if voice == f"{v.name} - Angry":
return v.angry
raise ValueError(f"Voice {voice} not found.")
def speed_from_text(voice, voices):
for v in voices:
if voice == f"{v.name} - Neutral":
return v.speed
if voice == f"{v.name} - Angry":
return v.speed
def run_tts_replicate(text: str, voice_character: str):
voice = voice_from_text(voice_character, voices_replicate)
input = {"text": text, "speaker": voice, "cleanup_voice": True}
output = replicate.run(
# "afiaka87/tortoise-tts:e9658de4b325863c4fcdc12d94bb7c9b54cbfe351b7ca1b36860008172b91c71",
"lucataco/xtts-v2:684bc3855b37866c0c65add2ff39c78f3dea3f4ff103a436465326e0f438d55e",
input=input,
)
logger.info(f"sound output: {output}")
return output
def get_fast_tts():
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = ParlerTTSForConditionalGeneration.from_pretrained(
"parler-tts/parler-tts-mini-expresso"
).to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-expresso")
return model, tokenizer, device
fast_tts = get_fast_tts()
def run_tts_fast(text: str):
model, tokenizer, device = fast_tts
description = "Thomas speaks moderately slowly in a sad tone with emphasis and high quality audio."
input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(text, return_tensors="pt").input_ids.to(device)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
return (model.config.sampling_rate, audio_arr), dict(text=text, voice="Thomas")
def load_melo_tts():
device = "cuda" if torch.cuda.is_available() else "cpu"
model = MeloTTS(language="EN", device=device)
return model
melo_tts = load_melo_tts()
def run_melo_tts(text: str, voice: str):
speed = 1.0
speaker_ids = melo_tts.hps.data.spk2id
audio = melo_tts.tts_to_file(text, speaker_ids["EN-Default"], None, speed=speed)
return melo_tts.hps.data.sampling_rate, audio
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