import os
os.system("git clone --branch v2.5 https://github.com/DigitalPhonetics/IMS-Toucan.git toucan_codebase")
os.system("mv toucan_codebase/* .")
from run_model_downloader import download_models
download_models()
import gradio as gr
import numpy as np
import torch
from InferenceInterfaces.UtteranceCloner import UtteranceCloner
from TrainingInterfaces.Text_to_Spectrogram.AutoAligner.Aligner import Aligner
from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.DurationCalculator import DurationCalculator
def float2pcm(sig, dtype='int16'):
"""
https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182
"""
sig = np.asarray(sig)
if sig.dtype.kind != 'f':
raise TypeError("'sig' must be a float array")
dtype = np.dtype(dtype)
if dtype.kind not in 'iu':
raise TypeError("'dtype' must be an integer type")
i = np.iinfo(dtype)
abs_max = 2 ** (i.bits - 1)
offset = i.min + abs_max
return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype)
class TTS_Interface:
def __init__(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.utterance_cloner = UtteranceCloner(model_id="Meta", device=self.device)
self.utterance_cloner.tts.set_language("de")
self.acoustic_model = Aligner()
self.acoustic_model.load_state_dict(torch.load("Models/Aligner/aligner.pt", map_location='cpu')["asr_model"])
self.acoustic_model = self.acoustic_model.to(self.device)
self.dc = DurationCalculator(reduction_factor=1)
self.text = "Quellen hattest du ihm, hattest dem Flüchtigen, kühle Schatten geschenkt, und die Gestade sahen, all ihm nach, und es bebte, aus den Wellen ihr lieblich Bild."
reference_audio = "reference_audios/2.wav"
self.duration, self.pitch, self.energy, _, _ = self.utterance_cloner.extract_prosody(self.text, reference_audio, lang="de", on_line_fine_tune=True)
self.phones = self.utterance_cloner.tts.text2phone.get_phone_string(self.text)
print(self.phones)
for index, phone in enumerate(self.phones):
print(index, phone)
#######
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_male.wav")
self.current_voice = "male"
self.cloned_speech_male = self.utterance_cloner.tts(self.phones,
view=False,
durations=self.duration,
pitch=self.pitch,
energy=self.energy,
input_is_phones=True).cpu().numpy()
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_female.wav")
self.current_voice = "female"
self.cloned_speech_female = self.utterance_cloner.tts(self.phones,
view=False,
durations=self.duration,
pitch=self.pitch,
energy=self.energy,
input_is_phones=True).cpu().numpy()
#######
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_male.wav")
self.current_voice = "male"
self.reg_speech_male = self.utterance_cloner.tts(
"Quellen hattest du ihm, hattest dem Flüchtigen kühle Schatten geschenkt, und die Gestade sahen all ihm nach, und es bebte aus den Wellen ihr lieblich Bild.",
view=False).cpu().numpy()
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_female.wav")
self.current_voice = "female"
self.reg_speech_female = self.utterance_cloner.tts(
"Quellen hattest du ihm, hattest dem Flüchtigen kühle Schatten geschenkt, und die Gestade sahen all ihm nach, und es bebte aus den Wellen ihr lieblich Bild.",
view=False).cpu().numpy()
def read(self, _, speaker, lengthening, pause_dur, pitch_up):
if speaker == "Female Voice" and self.current_voice != "female":
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_female.wav")
self.current_voice = "female"
elif speaker == "Male Voice" and self.current_voice != "male":
self.utterance_cloner.tts.set_utterance_embedding("reference_audios/german_male.wav")
self.current_voice = "male"
duration = self.duration.clone()
# lengthening
lenghtening_candidates = [
# ('f', 33),
# ('l', 34),
('ʏ', 35),
('ç', 36),
# ('t', 37),
('ɪ', 38),
# ('ɡ', 39),
('ə', 40),
('n', 41),
# ('z', 79),
('ɑ', 80),
# ('ə', 81),
('n', 82),
# ('b', 103),
('e', 104),
# ('p', 105),
# ('t', 106),
('ə', 107)
]
for lenghtening_candidate in lenghtening_candidates:
duration[lenghtening_candidate[1]] = duration[lenghtening_candidate[1]] + lengthening
# pauses
pause_candidates = [('~', 42),
('~', 83),
('~', 108)]
for pause_candidate in pause_candidates:
duration[pause_candidate[1]] = duration[pause_candidate[1]] + pause_dur
pitch = self.pitch.clone()
# pitch raise
pitch_candidates = [
# ('k', 44),
('y', 45),
('l', 46),
('ə', 47),
('ʃ', 49),
('a', 50),
('t', 51),
# ('ə', 52),
# ('n', 53),
('a', 85),
('l', 86),
('v', 118),
('ɛ', 119),
('l', 120),
# ('ə', 121),
# ('n', 122)
]
for pitch_candidate in pitch_candidates:
pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] + pitch_up
fixme = [('f', 33),
('l', 34),
('ʏ', 35),
('ç', 36),
('t', 37),
('ɪ', 38),
('ɡ', 39),
('ə', 40),
('n', 41)
]
for pitch_candidate in fixme:
pitch[pitch_candidate[1]] = pitch[pitch_candidate[1]] - abs(pitch_up)
manipulated_speech = self.utterance_cloner.tts(self.phones,
view=False,
durations=duration,
pitch=pitch,
energy=self.energy,
input_is_phones=True).cpu()
if self.current_voice == "female":
cloned_speech = self.cloned_speech_female
reg_speech = self.reg_speech_female
else:
cloned_speech = self.cloned_speech_male
reg_speech = self.reg_speech_male
return (24000, float2pcm(reg_speech)), (24000, float2pcm(cloned_speech)), (24000, float2pcm(manipulated_speech.numpy()))
poem_model = TTS_Interface()
article = "This is still a work in progress, models will be exchanged for better ones as soon as they are done. More diverse training data can help with more exact cloning and more controllability. For example we are still trying to incorporate more singing data.
Click here to learn more about the IMS Toucan Speech Synthesis Toolkit
"
iface = gr.Interface(fn=poem_model.read,
inputs=[gr.inputs.Dropdown([
"Quellen hattest du ihm, hattest dem Flüchtigen // kühle Schatten geschenkt, und die Gestade sahn // all ihm nach, und es bebte // aus den Wellen ihr lieblich Bild."],
type="value",
default="Quellen hattest du ihm, hattest dem Flüchtigen // kühle Schatten geschenkt, und die Gestade sahn // all ihm nach, und es bebte // aus den Wellen ihr lieblich Bild.",
label="Poem Transcript"),
gr.inputs.Dropdown(["Female Voice", "Male Voice"],
type="value",
default="Female Voice",
label="Select a Speaker"),
gr.inputs.Slider(minimum=0, maximum=4, step=1, default=2, label="Lengthening on verse end"),
gr.inputs.Slider(minimum=0, maximum=20, step=1, default=8, label="Length of Pause after verse end"),
gr.inputs.Slider(minimum=-0.4, maximum=0.4, step=0.01, default=0.2, label="Raise Pitch on new verse")
],
outputs=[gr.outputs.Audio(type="numpy", label="Poem read with prose reading"),
gr.outputs.Audio(type="numpy", label="Poem cloned from a reference"),
gr.outputs.Audio(type="numpy", label="Poem after human-in-the-loop adjustments")],
layout="vertical",
title="PoeticTTS - Customizing Poetry",
thumbnail="Utility/toucan.png",
theme="default",
allow_flagging="never",
allow_screenshot=False,
description="Customize how a poem is read by a text-to-speech system with intuitive high-level controls. You can control phrasing markers to go from prose style syntactic phrasing to verse aware poetry style phrasing with the sliders below.",
article=article)
iface.launch(enable_queue=True)