TTS_models.py

from abc import ABC, abstractmethod
import io

import numpy as np

import torch
from transformers import pipeline
from datasets import load_dataset

class TTSModel:
    def __init__(self, model_name):
        self.hf_name = model_name
        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"

    @abstractmethod
    def synthesize(self, text):
        pass

#####

####################################################
class SpeechT5(TTSModel):
    def __init__(self, name="microsoft/speecht5_tts"):
        super(SpeechT5, self).__init__(name)
        self.synthesiser = pipeline("text-to-speech", model=self.hf_name, device=self.device)
        self.embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
        self.speaker_embedding = torch.tensor(self.embeddings_dataset[7306]["xvector"]).unsqueeze(0)
        

    def synthesize(self, text):
        speech = self.synthesiser(text, forward_params={"speaker_embeddings": self.speaker_embedding})
        print("[SpeechT5 - synthesize]", speech)
        return (np.array(speech["audio"])* 32767).astype(np.int16) # return a numpy array of int to play

####################################################
# PENDING: NOT WORKING FROM HF
# from MeloTTS.melo.api import TTS as meloTTS
# import nltk

# class MeloTTS(TTSModel):
#     def __init__(self, name="myshell-ai/MeloTTS-English"):
#         super(MeloTTS, self).__init__(name)
#         nltk.download('averaged_perceptron_tagger_eng')
#         self.synthesiser = meloTTS(language='EN', device=self.device)
#         self.speaker_ids = self.synthesiser.hps.data.spk2id

#     def synthesize(self, text):
#         speech = self.synthesiser.tts_to_file(text, self.speaker_ids['EN-Default'])
#         print("[MeloTTS - synthesize]", speech)
#         return speech

####################################################
class Bark(TTSModel):
    def __init__(self, name="suno/bark"):
        super(Bark, self).__init__(name)
        self.synthesiser = pipeline("text-to-speech", model=self.hf_name, device=self.device)

    def synthesize(self, text):
        speech = self.synthesiser(text)
        print("[Bark - synthesize]", speech)
        return speech

####################################################
# pip install git+https://github.com/huggingface/parler-tts.git

from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer

class ParlerTTS(TTSModel):
    def __init__(self, name="parler-tts/parler-tts-large-v1"):
        super(ParlerTTS, self).__init__(name)
        self.description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
        self.model = ParlerTTSForConditionalGeneration.from_pretrained(self.hf_name).to(self.device)
        self.tokenizer = AutoTokenizer.from_pretrained(self.hf_name)
        # self.synthesiser = pipeline("text-to-speech", model=self.model, tokenizer=self.tokenizer, device=self.device)

    def synthesize(self, text):
        input_ids = self.tokenizer(self.description, return_tensors="pt").input_ids.to(self.device)
        prompt_input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to(self.device)

        generation = self.model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
        speech = generation.cpu().numpy().squeeze()
        print("[ParlerTTS - synthesize]", speech)
        return speech

####################################################
# PENDING: NOT WORKING FROM HF
# pip install coqui-tts
# https://github.com/idiap/coqui-ai-TTS

from TTS.api import TTS
class XTTS(TTSModel):
    def __init__(self, name="tts_models/en/ljspeech/glow-tts"):
        super(XTTS, self).__init__(name)
        self.synthesiser = TTS(model_name=name, progress_bar=False).to(self.device)
        # self.model = AutoModelForSequenceClassification.from_pretrained(self.hf_name).to(self.device)
        # self.tokenizer = AutoTokenizer.from_pretrained(self.hf_name)
        # self.synthesiser = pipeline("text-to-speech", model=self.model, tokenizer=self.tokenizer, device=self.device)

        # self.synthesiser = pipeline("text-to-speech", model=self.hf_name, device=self.device)


    def synthesize(self, text):
        # input_ids = self.tokenizer(self.description, return_tensors="pt").input_ids.to(self.device)
        # prompt_input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to(self.device)
        # print("synthesizing ", text)
        speech = self.synthesiser.tts(text=text)
        print("[XTTS - synthesize]", len(speech), text)
        return speech