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controllable_talknet.py
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#!/usr/bin/env python3
import os
import base64
import dash
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.exceptions import PreventUpdate
import torch
import numpy as np
import tensorflow as tf
from scipy.io import wavfile
import io
from nemo.collections.tts.models import TalkNetSpectModel
from nemo.collections.tts.models import TalkNetPitchModel
from nemo.collections.tts.models import TalkNetDursModel
from core.talknet_singer import TalkNetSingerModel
import json
import traceback
import ffmpeg
import time
import uuid
from core import extract, vocoder, reconstruct
from core.download import download_from_drive
from flask import request, send_file, make_response, abort
app = JupyterDash(__name__)
def getSilentWav():
f = open("assets/silent.wav", "rb")
buffer = io.BytesIO(f.read())
return buffer
def mkResponse(data):
return make_response(send_file(
data,
attachment_filename="audio.wav",
mimetype="audio/x-wav",
))
@app.server.route("/tts")
def home():
s = request.args.get("s")
response = None
if s is None or s == "":
response = mkResponse(getSilentWav())
else:
voice = request.args.get('voice')
if voice is None or voice == "":
voice = "1k3EMXxLC0fLvfxzGbeP6B6plgu9hqCSx"
response = mkResponse(generate_audio2(1, voice + "|default", None, s, [], 0, None, None, None))
# res.setHeader('Access-Control-Allow-Origin', '*');
# res.setHeader('Access-Control-Allow-Headers', '*');
# res.setHeader('Access-Control-Allow-Methods', '*');
# res.setHeader('Cross-Origin-Opener-Policy', 'same-origin');
# res.setHeader('Cross-Origin-Embedder-Policy', 'require-corp');
# res.setHeader('Cross-Origin-Resource-Policy', 'cross-origin');
response.headers["Access-Control-Allow-Origin"] = "*"
response.headers["Access-Control-Allow-Headers"] = "*"
response.headers["Access-Control-Allow-Methods"] = "*"
return response
DEVICE = "cuda:0"
CPU_PITCH = False
RUN_PATH = os.path.dirname(os.path.realpath(__file__))
if RUN_PATH == "/content":
UI_MODE = "colab"
elif os.path.exists("/talknet/is_docker"):
UI_MODE = "docker"
else:
UI_MODE = "offline"
torch.set_grad_enabled(False)
if CPU_PITCH:
tf.config.set_visible_devices([], "GPU")
extract_dur = extract.ExtractDuration(RUN_PATH, DEVICE)
extract_pitch = extract.ExtractPitch()
reconstruct_inst = reconstruct.Reconstruct(
DEVICE,
os.path.join(
RUN_PATH,
"core",
"vqgan_config.yaml",
),
os.path.join(
RUN_PATH,
"models",
"vqgan32_universal_57000.ckpt",
),
)
app.title = "Webaverse TTS"
app.layout = html.Div(
children=[
html.H1(
id="header",
children="Webaverse TTS",
style={
"color": "#52042c",
"font-size": "4em",
"text-align": "center",
"margin-top": "0em",
"margin-bottom": "0em",
},
),
html.Label("Character selection", htmlFor="model-dropdown"),
dbc.Select(
id="model-dropdown",
options=[
{
"label": "Custom model",
"value": "Custom",
},
{
"label": "--- ERROR LOADING MODEL LISTS ---",
"value": "",
"disabled": True,
},
],
value=None,
style={
"max-width": "90vw",
"width": "35em",
"margin-bottom": "0.7em",
},
),
html.Div(
children=[
dcc.Input(
id="drive-id",
type="text",
placeholder="Drive ID for custom model",
style={"width": "22em"},
),
],
id="custom-model",
style={
"display": "none",
},
),
dcc.Store(id="current-f0s"),
dcc.Store(id="current-f0s-nosilence"),
html.Label("Transcript", htmlFor="transcript-input"),
dcc.Textarea(
id="transcript-input",
value="",
style={
"max-width": "90vw",
"width": "50em",
"height": "8em",
"margin-bottom": "0.7em",
},
),
dcc.Loading(
html.Div(
[
html.Button(
"Generate",
id="gen-button",
),
html.Audio(
id="audio-out",
controls=True,
style={
"display": "none",
},
),
html.Div(
id="generated-info",
style={
"font-style": "italic",
},
),
],
style={
"width": "100%",
"display": "flex",
"align-items": "center",
"justify-content": "center",
"flex-direction": "column",
},
)
)
],
style={
"width": "100%",
"display": "flex",
"align-items": "center",
"justify-content": "center",
"flex-direction": "column",
"background-color": "#FFF",
},
)
upload_display = {
"width": "100%",
"height": "60px",
"lineHeight": "60px",
"borderWidth": "1px",
"borderStyle": "dashed",
"borderRadius": "5px",
"textAlign": "center",
"margin": "10px",
}
playback_style = {
"margin-top": "0.3em",
"margin-bottom": "0.3em",
"display": "block",
"width": "600px",
"max-width": "90vw",
}
playback_hide = {
"display": "none",
}
@app.callback(
[
dash.dependencies.Output("model-dropdown", "options"),
dash.dependencies.Output("upload-audio", "style"),
],
dash.dependencies.Input("header", "children"),
)
def init_dropdown(value):
if UI_MODE == "docker":
upload_style = upload_display
else:
upload_style = playback_hide
dropdown = [
{
"label": "Custom model",
"value": "Custom|default",
}
]
prev_values = ["Custom|default"]
def add_to_dropdown(entry):
if entry["value"] in prev_values:
return
dropdown.append(entry)
prev_values.append(entry["value"])
all_dict = {}
for filename in os.listdir("model_lists"):
if len(filename) < 5 or filename[-5:].lower() != ".json":
continue
with open(os.path.join("model_lists", filename)) as f:
j = json.load(f)
for s in j:
for c in s["characters"]:
c["source_file"] = filename[:-5]
if s["source"] not in all_dict:
all_dict[s["source"]] = s["characters"]
else:
all_dict[s["source"]].extend(s["characters"])
for k in sorted(all_dict):
seen_chars = []
seen_ids = []
characters = {}
characters_sing = {}
has_singers = False
for c in all_dict[k]:
if c["drive_id"] in seen_ids:
continue
seen_ids.append(c["drive_id"])
# Handle duplicate names
if c["name"] in seen_chars:
if c["name"] in characters:
rename = (
c["name"] + " [" + characters[c["name"]]["source_file"] + "]"
)
characters[rename] = characters[c["name"]]
del characters[c["name"]]
c["name"] = c["name"] + " [" + c["source_file"] + "]"
else:
seen_chars.append(c["name"])
characters[c["name"]] = {
"drive_id": c["drive_id"],
"is_singing": c["is_singing"],
"source_file": c["source_file"],
}
if c["is_singing"]:
has_singers = True
if has_singers:
for ck in sorted(characters):
if characters[ck]["is_singing"]:
characters_sing[ck] = characters[ck]
del characters[ck]
separator = "--- " + k.strip().upper() + " MODELS (TALKING) ---"
else:
separator = "--- " + k.strip().upper() + " MODELS ---"
if len(characters) > 0:
add_to_dropdown(
{
"label": separator,
"value": str(uuid.uuid4()) + "|default",
"disabled": True,
}
)
for ck in sorted(characters):
add_to_dropdown(
{
"label": ck,
"value": characters[ck]["drive_id"] + "|default",
}
)
if has_singers:
separator = "--- " + k.strip().upper() + " MODELS (SINGING) ---"
add_to_dropdown(
{
"label": separator,
"value": str(uuid.uuid4()) + "|default",
"disabled": True,
}
)
for ck in sorted(characters_sing):
add_to_dropdown(
{
"label": ck,
"value": characters_sing[ck]["drive_id"] + "|singing",
}
)
if len(all_dict) == 0:
add_to_dropdown(
{
"label": "--- NO MODEL LISTS FOUND ---",
"value": str(uuid.uuid4()) + "|default",
"disabled": True,
}
)
return [dropdown, upload_style]
@app.callback(
dash.dependencies.Output("custom-model", "style"),
dash.dependencies.Input("model-dropdown", "value"),
)
def update_model(model):
if model is not None and model.split("|")[0] == "Custom":
style = {"margin-bottom": "0.7em", "display": "block"}
else:
style = {"display": "none"}
return style
tnmodels, tnmodel, tnpath, tndurs, tnpitch = {}, None, None, None, None
voc, last_voc, sr_voc, rec_voc = None, None, None, None
@app.callback(
[
dash.dependencies.Output("audio-out", "src"),
dash.dependencies.Output("generated-info", "children"),
dash.dependencies.Output("audio-out", "style"),
dash.dependencies.Output("audio-out", "title"),
],
[dash.dependencies.Input("gen-button", "n_clicks")],
[
dash.dependencies.State("model-dropdown", "value"),
dash.dependencies.State("drive-id", "value"),
dash.dependencies.State("transcript-input", "value"),
dash.dependencies.State("pitch-options", "value"),
dash.dependencies.State("pitch-factor", "value"),
dash.dependencies.State("current-filename", "data"),
dash.dependencies.State("current-f0s", "data"),
dash.dependencies.State("current-f0s-nosilence", "data"),
],
)
def generate_audio(
n_clicks,
model,
custom_model,
transcript,
pitch_options,
pitch_factor,
wav_name,
f0s,
f0s_wo_silence,
):
global tnmodels, tnmodel, tnpath, tndurs, tnpitch, voc, last_voc, sr_voc, rec_voc
# n_clicks, model, custom_model, transcript, pitch_options, pitch_factor, wav_name, f0s, f0s_wo_silence,
# 1 1UFQWJHzKFPumoxioopPbAzM9ydznnRX3|default None "fsdg" ['dra'] 0 None None None
# 1 132G6oD0HHPPn4t1H6IkYv18_F0UVLWgi|default None "got it and lol" ['dra'] 0 None None None
# with open('readme.txt', 'w') as f:
# f.write("%s %s %s %s %s %s %s %s %s" % (n_clicks, model, custom_model, transcript, pitch_options, pitch_factor, wav_name, f0s, f0s_wo_silence))
if n_clicks is None:
raise PreventUpdate
if model is None:
return [None, "No character selected", playback_hide, None]
if transcript is None or transcript.strip() == "":
return [
None,
"No transcript entered",
playback_hide,
None,
]
load_error, talknet_path, vocoder_path = download_from_drive(
model.split("|")[0], custom_model, RUN_PATH
)
if load_error is not None:
return [
None,
load_error,
playback_hide,
None,
]
try:
with torch.no_grad():
tnmodel = tnmodels.get(talknet_path)
if tnmodel is None:
# if tnpath != talknet_path:
singer_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetSinger.nemo"
)
if os.path.exists(singer_path):
tnmodel = TalkNetSingerModel.restore_from(singer_path)
else:
tnmodel = TalkNetSpectModel.restore_from(talknet_path)
durs_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetDurs.nemo"
)
pitch_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetPitch.nemo"
)
if os.path.exists(durs_path):
tndurs = TalkNetDursModel.restore_from(durs_path)
tnmodel.add_module("_durs_model", tndurs)
tnpitch = TalkNetPitchModel.restore_from(pitch_path)
tnmodel.add_module("_pitch_model", tnpitch)
else:
tndurs = None
tnpitch = None
tnmodel.eval()
tnpath = talknet_path
tnmodels[talknet_path] = tnmodel
# Generate spectrogram
token_list, tokens, arpa = extract_dur.get_tokens(transcript)
if tndurs is None or tnpitch is None:
return [
None,
"Model doesn't support pitch prediction",
playback_hide,
None,
]
# Vocoding
if last_voc != vocoder_path:
voc = vocoder.HiFiGAN(vocoder_path, "config_v1", DEVICE)
last_voc = vocoder_path
audio, audio_torch = voc.vocode(spect)
# Reconstruction
if "srec" in pitch_options:
new_spect = reconstruct_inst.reconstruct(spect)
if rec_voc is None:
rec_voc = vocoder.HiFiGAN(
os.path.join(RUN_PATH, "models", "hifirec"), "config_v1", DEVICE
)
audio, audio_torch = rec_voc.vocode(new_spect)
# Super-resolution
if sr_voc is None:
sr_voc = vocoder.HiFiGAN(
os.path.join(RUN_PATH, "models", "hifisr"), "config_32k", DEVICE
)
sr_mix, new_rate = sr_voc.superres(audio, 22050)
# Create buffer
buffer = io.BytesIO()
wavfile.write(buffer, new_rate, sr_mix.astype(np.int16))
b64 = base64.b64encode(buffer.getvalue())
sound = "data:audio/x-wav;base64," + b64.decode("ascii")
output_name = "TalkNet_" + str(int(time.time()))
return [sound, arpa, playback_style, output_name]
except Exception:
return [
None,
str(traceback.format_exc()),
playback_hide,
None,
]
def generate_audio2(
n_clicks,
model,
custom_model,
transcript,
pitch_options,
pitch_factor,
wav_name,
f0s,
f0s_wo_silence,
):
global tnmodels, tnpath, tndurs, tnpitch, voc, last_voc, sr_voc, rec_voc
# n_clicks, model, custom_model, transcript, pitch_options, pitch_factor, wav_name, f0s, f0s_wo_silence,
# 1 1UFQWJHzKFPumoxioopPbAzM9ydznnRX3|default None "fsdg" ['dra'] 0 None None None
# 1 132G6oD0HHPPn4t1H6IkYv18_F0UVLWgi|default None "got it and lol" ['dra'] 0 None None None
# with open('readme.txt', 'w') as f:
# f.write("%s %s %s %s %s %s %s %s %s" % (n_clicks, model, custom_model, transcript, pitch_options, pitch_factor, wav_name, f0s, f0s_wo_silence))
if n_clicks is None:
raise PreventUpdate
if model is None:
return [None, "No character selected", playback_hide, None]
if transcript is None or transcript.strip() == "":
return [
None,
"No transcript entered",
playback_hide,
None,
]
load_error, talknet_path, vocoder_path = download_from_drive(
model.split("|")[0], custom_model, RUN_PATH
)
if load_error is not None:
return [
None,
load_error,
playback_hide,
None,
]
try:
with torch.no_grad():
tnmodel = tnmodels.get(talknet_path)
if tnmodel is None:
# if tnpath != talknet_path:
singer_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetSinger.nemo"
)
if os.path.exists(singer_path):
tnmodel = TalkNetSingerModel.restore_from(singer_path)
else:
tnmodel = TalkNetSpectModel.restore_from(talknet_path)
durs_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetDurs.nemo"
)
pitch_path = os.path.join(
os.path.dirname(talknet_path), "TalkNetPitch.nemo"
)
if os.path.exists(durs_path):
tndurs = TalkNetDursModel.restore_from(durs_path)
tnmodel.add_module("_durs_model", tndurs)
tnpitch = TalkNetPitchModel.restore_from(pitch_path)
tnmodel.add_module("_pitch_model", tnpitch)
else:
tndurs = None
tnpitch = None
tnmodel.eval()
tnpath = talknet_path
tnmodels[talknet_path] = tnmodel
# Generate spectrogram
try:
token_list, tokens, arpa = extract_dur.get_tokens(transcript)
if tndurs is None or tnpitch is None:
return [
None,
"Model doesn't support pitch prediction",
playback_hide,
None,
]
spect = tnmodel.generate_spectrogram(tokens=tokens)
# Vocoding
if last_voc != vocoder_path:
voc = vocoder.HiFiGAN(vocoder_path, "config_v1", DEVICE)
last_voc = vocoder_path
audio, audio_torch = voc.vocode(spect)
# Reconstruction
if "srec" in pitch_options:
new_spect = reconstruct_inst.reconstruct(spect)
if rec_voc is None:
rec_voc = vocoder.HiFiGAN(
os.path.join(RUN_PATH, "models", "hifirec"), "config_v1", DEVICE
)
audio, audio_torch = rec_voc.vocode(new_spect)
# Super-resolution
if sr_voc is None:
sr_voc = vocoder.HiFiGAN(
os.path.join(RUN_PATH, "models", "hifisr"), "config_32k", DEVICE
)
sr_mix, new_rate = sr_voc.superres(audio, 22050)
# Create buffer
buffer = io.BytesIO()
wavfile.write(buffer, new_rate, sr_mix.astype(np.int16))
return buffer
# b64 = base64.b64encode(buffer.getvalue())
# sound = "data:audio/x-wav;base64," + b64.decode("ascii")
# output_name = "TalkNet_" + str(int(time.time()))
# return [sound, arpa, playback_style, output_name]
except IndexError:
return getSilentWav()
except Exception:
return str(traceback.format_exc())
# get port from env
port = int(os.environ.get("PORT", 8888))
if __name__ == "__main__":
app.run_server(
host="0.0.0.0",
port=port,
mode="external",
debug=False,
# dev_tools_silence_routes_logging = False,
# dev_tools_ui=True,
# dev_tools_hot_reload=True,
threaded=True,
)