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import os
import sys
import tempfile
import random
from transformers import pipeline
import gradio as gr
import torch
import gc
import click
import torchaudio
from glob import glob
import librosa
import numpy as np
from scipy.io import wavfile
import shutil
import time
import json
from model.utils import convert_char_to_pinyin
import signal
import psutil
import platform
import subprocess
from datasets.arrow_writer import ArrowWriter
from datasets import Dataset as Dataset_
from api import F5TTS
training_process = None
system = platform.system()
python_executable = sys.executable or "python"
tts_api = None
last_checkpoint = ""
last_device = ""
path_data = "data"
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
pipe = None
# Load metadata
def get_audio_duration(audio_path):
"""Calculate the duration of an audio file."""
audio, sample_rate = torchaudio.load(audio_path)
num_channels = audio.shape[0]
return audio.shape[1] / (sample_rate * num_channels)
def clear_text(text):
"""Clean and prepare text by lowering the case and stripping whitespace."""
return text.lower().strip()
def get_rms(
y,
frame_length=2048,
hop_length=512,
pad_mode="constant",
): # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
# put our new within-frame axis at the end for now
out_strides = y.strides + tuple([y.strides[axis]])
# Reduce the shape on the framing axis
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
if axis < 0:
target_axis = axis - 1
else:
target_axis = axis + 1
xw = np.moveaxis(xw, -1, target_axis)
# Downsample along the target axis
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
# Calculate power
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
def __init__(
self,
sr: int,
threshold: float = -40.0,
min_length: int = 2000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 2000,
):
if not min_length >= min_interval >= hop_size:
raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
if not max_sil_kept >= hop_size:
raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.0)
self.hop_size = round(sr * hop_size / 1000)
self.win_size = min(round(min_interval), 4 * self.hop_size)
self.min_length = round(sr * min_length / 1000 / self.hop_size)
self.min_interval = round(min_interval / self.hop_size)
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
def _apply_slice(self, waveform, begin, end):
if len(waveform.shape) > 1:
return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
else:
return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
# @timeit
def slice(self, waveform):
if len(waveform.shape) > 1:
samples = waveform.mean(axis=0)
else:
samples = waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
for i, rms in enumerate(rms_list):
# Keep looping while frame is silent.
if rms < self.threshold:
# Record start of silent frames.
if silence_start is None:
silence_start = i
continue
# Keep looping while frame is not silent and silence start has not been recorded.
if silence_start is None:
continue
# Clear recorded silence start if interval is not enough or clip is too short
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
# Need slicing. Record the range of silent frames to be removed.
if i - silence_start <= self.max_sil_kept:
pos = rms_list[silence_start : i + 1].argmin() + silence_start
if silence_start == 0:
sil_tags.append((0, pos))
else:
sil_tags.append((pos, pos))
clip_start = pos
elif i - silence_start <= self.max_sil_kept * 2:
pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
pos += i - self.max_sil_kept
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
clip_start = pos_r
else:
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
clip_start = max(pos_r, pos)
else:
pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
else:
sil_tags.append((pos_l, pos_r))
clip_start = pos_r
silence_start = None
# Deal with trailing silence.
total_frames = rms_list.shape[0]
if silence_start is not None and total_frames - silence_start >= self.min_interval:
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
# Apply and return slices.
####ι³ι’+θ΅·ε§ζΆι΄+η»ζ’ζΆι΄
if len(sil_tags) == 0:
return [[waveform, 0, int(total_frames * self.hop_size)]]
else:
chunks = []
if sil_tags[0][0] > 0:
chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
for i in range(len(sil_tags) - 1):
chunks.append(
[
self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
int(sil_tags[i][1] * self.hop_size),
int(sil_tags[i + 1][0] * self.hop_size),
]
)
if sil_tags[-1][1] < total_frames:
chunks.append(
[
self._apply_slice(waveform, sil_tags[-1][1], total_frames),
int(sil_tags[-1][1] * self.hop_size),
int(total_frames * self.hop_size),
]
)
return chunks
# terminal
def terminate_process_tree(pid, including_parent=True):
try:
parent = psutil.Process(pid)
except psutil.NoSuchProcess:
# Process already terminated
return
children = parent.children(recursive=True)
for child in children:
try:
os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL
except OSError:
pass
if including_parent:
try:
os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL
except OSError:
pass
def terminate_process(pid):
if system == "Windows":
cmd = f"taskkill /t /f /pid {pid}"
os.system(cmd)
else:
terminate_process_tree(pid)
def start_training(
dataset_name="",
exp_name="F5TTS_Base",
learning_rate=1e-4,
batch_size_per_gpu=400,
batch_size_type="frame",
max_samples=64,
grad_accumulation_steps=1,
max_grad_norm=1.0,
epochs=11,
num_warmup_updates=200,
save_per_updates=400,
last_per_steps=800,
finetune=True,
):
global training_process, tts_api
if tts_api is not None:
del tts_api
gc.collect()
torch.cuda.empty_cache()
tts_api = None
path_project = os.path.join(path_data, dataset_name + "_pinyin")
if not os.path.isdir(path_project):
yield (
f"There is not project with name {dataset_name}",
gr.update(interactive=True),
gr.update(interactive=False),
)
return
file_raw = os.path.join(path_project, "raw.arrow")
if not os.path.isfile(file_raw):
yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
return
# Check if a training process is already running
if training_process is not None:
return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
yield "start train", gr.update(interactive=False), gr.update(interactive=False)
# Command to run the training script with the specified arguments
cmd = (
f"accelerate launch finetune-cli.py --exp_name {exp_name} "
f"--learning_rate {learning_rate} "
f"--batch_size_per_gpu {batch_size_per_gpu} "
f"--batch_size_type {batch_size_type} "
f"--max_samples {max_samples} "
f"--grad_accumulation_steps {grad_accumulation_steps} "
f"--max_grad_norm {max_grad_norm} "
f"--epochs {epochs} "
f"--num_warmup_updates {num_warmup_updates} "
f"--save_per_updates {save_per_updates} "
f"--last_per_steps {last_per_steps} "
f"--dataset_name {dataset_name}"
)
if finetune:
cmd += f" --finetune {finetune}"
print(cmd)
try:
# Start the training process
training_process = subprocess.Popen(cmd, shell=True)
time.sleep(5)
yield "train start", gr.update(interactive=False), gr.update(interactive=True)
# Wait for the training process to finish
training_process.wait()
time.sleep(1)
if training_process is None:
text_info = "train stop"
else:
text_info = "train complete !"
except Exception as e: # Catch all exceptions
# Ensure that we reset the training process variable in case of an error
text_info = f"An error occurred: {str(e)}"
training_process = None
yield text_info, gr.update(interactive=True), gr.update(interactive=False)
def stop_training():
global training_process
if training_process is None:
return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
terminate_process_tree(training_process.pid)
training_process = None
return "train stop", gr.update(interactive=True), gr.update(interactive=False)
def create_data_project(name):
name += "_pinyin"
os.makedirs(os.path.join(path_data, name), exist_ok=True)
os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
def transcribe(file_audio, language="english"):
global pipe
if pipe is None:
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large-v3-turbo",
torch_dtype=torch.float16,
device=device,
)
text_transcribe = pipe(
file_audio,
chunk_length_s=30,
batch_size=128,
generate_kwargs={"task": "transcribe", "language": language},
return_timestamps=False,
)["text"].strip()
return text_transcribe
def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
name_project += "_pinyin"
path_project = os.path.join(path_data, name_project)
path_dataset = os.path.join(path_project, "dataset")
path_project_wavs = os.path.join(path_project, "wavs")
file_metadata = os.path.join(path_project, "metadata.csv")
if audio_files is None:
return "You need to load an audio file."
if os.path.isdir(path_project_wavs):
shutil.rmtree(path_project_wavs)
if os.path.isfile(file_metadata):
os.remove(file_metadata)
os.makedirs(path_project_wavs, exist_ok=True)
if user:
file_audios = [
file
for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
for file in glob(os.path.join(path_dataset, format))
]
if file_audios == []:
return "No audio file was found in the dataset."
else:
file_audios = audio_files
alpha = 0.5
_max = 1.0
slicer = Slicer(24000)
num = 0
error_num = 0
data = ""
for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
audio, _ = librosa.load(file_audio, sr=24000, mono=True)
list_slicer = slicer.slice(audio)
for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
name_segment = os.path.join(f"segment_{num}")
file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
tmp_max = np.abs(chunk).max()
if tmp_max > 1:
chunk /= tmp_max
chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
try:
text = transcribe(file_segment, language)
text = text.lower().strip().replace('"', "")
data += f"{name_segment}|{text}\n"
num += 1
except: # noqa: E722
error_num += 1
with open(file_metadata, "w", encoding="utf-8") as f:
f.write(data)
if error_num != []:
error_text = f"\nerror files : {error_num}"
else:
error_text = ""
return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
def format_seconds_to_hms(seconds):
hours = int(seconds / 3600)
minutes = int((seconds % 3600) / 60)
seconds = seconds % 60
return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
def create_metadata(name_project, progress=gr.Progress()):
name_project += "_pinyin"
path_project = os.path.join(path_data, name_project)
path_project_wavs = os.path.join(path_project, "wavs")
file_metadata = os.path.join(path_project, "metadata.csv")
file_raw = os.path.join(path_project, "raw.arrow")
file_duration = os.path.join(path_project, "duration.json")
file_vocab = os.path.join(path_project, "vocab.txt")
if not os.path.isfile(file_metadata):
return "The file was not found in " + file_metadata
with open(file_metadata, "r", encoding="utf-8") as f:
data = f.read()
audio_path_list = []
text_list = []
duration_list = []
count = data.split("\n")
lenght = 0
result = []
error_files = []
for line in progress.tqdm(data.split("\n"), total=count):
sp_line = line.split("|")
if len(sp_line) != 2:
continue
name_audio, text = sp_line[:2]
file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
if not os.path.isfile(file_audio):
error_files.append(file_audio)
continue
duraction = get_audio_duration(file_audio)
if duraction < 2 and duraction > 15:
continue
if len(text) < 4:
continue
text = clear_text(text)
text = convert_char_to_pinyin([text], polyphone=True)[0]
audio_path_list.append(file_audio)
duration_list.append(duraction)
text_list.append(text)
result.append({"audio_path": file_audio, "text": text, "duration": duraction})
lenght += duraction
if duration_list == []:
error_files_text = "\n".join(error_files)
return f"Error: No audio files found in the specified path : \n{error_files_text}"
min_second = round(min(duration_list), 2)
max_second = round(max(duration_list), 2)
with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
for line in progress.tqdm(result, total=len(result), desc="prepare data"):
writer.write(line)
with open(file_duration, "w", encoding="utf-8") as f:
json.dump({"duration": duration_list}, f, ensure_ascii=False)
file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"
if not os.path.isfile(file_vocab_finetune):
return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
shutil.copy2(file_vocab_finetune, file_vocab)
if error_files != []:
error_text = "error files\n" + "\n".join(error_files)
else:
error_text = ""
return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}"
def check_user(value):
return gr.update(visible=not value), gr.update(visible=value)
def calculate_train(
name_project,
batch_size_type,
max_samples,
learning_rate,
num_warmup_updates,
save_per_updates,
last_per_steps,
finetune,
):
name_project += "_pinyin"
path_project = os.path.join(path_data, name_project)
file_duraction = os.path.join(path_project, "duration.json")
if not os.path.isfile(file_duraction):
return (
1000,
max_samples,
num_warmup_updates,
save_per_updates,
last_per_steps,
"project not found !",
learning_rate,
)
with open(file_duraction, "r") as file:
data = json.load(file)
duration_list = data["duration"]
samples = len(duration_list)
if torch.cuda.is_available():
gpu_properties = torch.cuda.get_device_properties(0)
total_memory = gpu_properties.total_memory / (1024**3)
elif torch.backends.mps.is_available():
total_memory = psutil.virtual_memory().available / (1024**3)
if batch_size_type == "frame":
batch = int(total_memory * 0.5)
batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
batch_size_per_gpu = int(38400 / batch)
else:
batch_size_per_gpu = int(total_memory / 8)
batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
batch = batch_size_per_gpu
if batch_size_per_gpu <= 0:
batch_size_per_gpu = 1
if samples < 64:
max_samples = int(samples * 0.25)
else:
max_samples = 64
num_warmup_updates = int(samples * 0.05)
save_per_updates = int(samples * 0.10)
last_per_steps = int(save_per_updates * 5)
max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
if finetune:
learning_rate = 1e-5
else:
learning_rate = 7.5e-5
return batch_size_per_gpu, max_samples, num_warmup_updates, save_per_updates, last_per_steps, samples, learning_rate
def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -> None:
try:
checkpoint = torch.load(checkpoint_path)
print("Original Checkpoint Keys:", checkpoint.keys())
ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
if ema_model_state_dict is not None:
new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
torch.save(new_checkpoint, new_checkpoint_path)
return f"New checkpoint saved at: {new_checkpoint_path}"
else:
return "No 'ema_model_state_dict' found in the checkpoint."
except Exception as e:
return f"An error occurred: {e}"
def vocab_check(project_name):
name_project = project_name + "_pinyin"
path_project = os.path.join(path_data, name_project)
file_metadata = os.path.join(path_project, "metadata.csv")
file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt"
if not os.path.isfile(file_vocab):
return f"the file {file_vocab} not found !"
with open(file_vocab, "r", encoding="utf-8") as f:
data = f.read()
vocab = data.split("\n")
if not os.path.isfile(file_metadata):
return f"the file {file_metadata} not found !"
with open(file_metadata, "r", encoding="utf-8") as f:
data = f.read()
miss_symbols = []
miss_symbols_keep = {}
for item in data.split("\n"):
sp = item.split("|")
if len(sp) != 2:
continue
text = sp[1].lower().strip()
for t in text:
if t not in vocab and t not in miss_symbols_keep:
miss_symbols.append(t)
miss_symbols_keep[t] = t
if miss_symbols == []:
info = "You can train using your language !"
else:
info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)
return info
def get_random_sample_prepare(project_name):
name_project = project_name + "_pinyin"
path_project = os.path.join(path_data, name_project)
file_arrow = os.path.join(path_project, "raw.arrow")
if not os.path.isfile(file_arrow):
return "", None
dataset = Dataset_.from_file(file_arrow)
random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
audio_path = random_sample["audio_path"][0]
return text, audio_path
def get_random_sample_transcribe(project_name):
name_project = project_name + "_pinyin"
path_project = os.path.join(path_data, name_project)
file_metadata = os.path.join(path_project, "metadata.csv")
if not os.path.isfile(file_metadata):
return "", None
data = ""
with open(file_metadata, "r", encoding="utf-8") as f:
data = f.read()
list_data = []
for item in data.split("\n"):
sp = item.split("|")
if len(sp) != 2:
continue
list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]])
if list_data == []:
return "", None
random_item = random.choice(list_data)
return random_item[1], random_item[0]
def get_random_sample_infer(project_name):
text, audio = get_random_sample_transcribe(project_name)
return (
text,
text,
audio,
)
def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step):
global last_checkpoint, last_device, tts_api
if not os.path.isfile(file_checkpoint):
return None
if training_process is not None:
device_test = "cpu"
else:
device_test = None
if last_checkpoint != file_checkpoint or last_device != device_test:
if last_checkpoint != file_checkpoint:
last_checkpoint = file_checkpoint
if last_device != device_test:
last_device = device_test
tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test)
print("update", device_test, file_checkpoint)
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name)
return f.name
with gr.Blocks() as app:
with gr.Row():
project_name = gr.Textbox(label="project name", value="my_speak")
bt_create = gr.Button("create new project")
bt_create.click(fn=create_data_project, inputs=[project_name])
with gr.Tabs():
with gr.TabItem("transcribe Data"):
ch_manual = gr.Checkbox(label="user", value=False)
mark_info_transcribe = gr.Markdown(
"""```plaintext
Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory.
my_speak/
β
βββ dataset/
βββ audio1.wav
βββ audio2.wav
...
```""",
visible=False,
)
audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple")
txt_lang = gr.Text(label="Language", value="english")
bt_transcribe = bt_create = gr.Button("transcribe")
txt_info_transcribe = gr.Text(label="info", value="")
bt_transcribe.click(
fn=transcribe_all,
inputs=[project_name, audio_speaker, txt_lang, ch_manual],
outputs=[txt_info_transcribe],
)
ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
random_sample_transcribe = gr.Button("random sample")
with gr.Row():
random_text_transcribe = gr.Text(label="Text")
random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
random_sample_transcribe.click(
fn=get_random_sample_transcribe,
inputs=[project_name],
outputs=[random_text_transcribe, random_audio_transcribe],
)
with gr.TabItem("prepare Data"):
gr.Markdown(
"""```plaintext
place all your wavs folder and your metadata.csv file in {your name project}
my_speak/
β
βββ wavs/
β βββ audio1.wav
β βββ audio2.wav
| ...
β
βββ metadata.csv
file format metadata.csv
audio1|text1
audio2|text1
...
```"""
)
bt_prepare = bt_create = gr.Button("prepare")
txt_info_prepare = gr.Text(label="info", value="")
bt_prepare.click(fn=create_metadata, inputs=[project_name], outputs=[txt_info_prepare])
random_sample_prepare = gr.Button("random sample")
with gr.Row():
random_text_prepare = gr.Text(label="Pinyin")
random_audio_prepare = gr.Audio(label="Audio", type="filepath")
random_sample_prepare.click(
fn=get_random_sample_prepare, inputs=[project_name], outputs=[random_text_prepare, random_audio_prepare]
)
with gr.TabItem("train Data"):
with gr.Row():
bt_calculate = bt_create = gr.Button("Auto Settings")
ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True)
lb_samples = gr.Label(label="samples")
batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
with gr.Row():
exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
with gr.Row():
batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
max_samples = gr.Number(label="Max Samples", value=64)
with gr.Row():
grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
with gr.Row():
epochs = gr.Number(label="Epochs", value=10)
num_warmup_updates = gr.Number(label="Warmup Updates", value=5)
with gr.Row():
save_per_updates = gr.Number(label="Save per Updates", value=10)
last_per_steps = gr.Number(label="Last per Steps", value=50)
with gr.Row():
start_button = gr.Button("Start Training")
stop_button = gr.Button("Stop Training", interactive=False)
txt_info_train = gr.Text(label="info", value="")
start_button.click(
fn=start_training,
inputs=[
project_name,
exp_name,
learning_rate,
batch_size_per_gpu,
batch_size_type,
max_samples,
grad_accumulation_steps,
max_grad_norm,
epochs,
num_warmup_updates,
save_per_updates,
last_per_steps,
ch_finetune,
],
outputs=[txt_info_train, start_button, stop_button],
)
stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
bt_calculate.click(
fn=calculate_train,
inputs=[
project_name,
batch_size_type,
max_samples,
learning_rate,
num_warmup_updates,
save_per_updates,
last_per_steps,
ch_finetune,
],
outputs=[
batch_size_per_gpu,
max_samples,
num_warmup_updates,
save_per_updates,
last_per_steps,
lb_samples,
learning_rate,
],
)
with gr.TabItem("reduse checkpoint"):
txt_path_checkpoint = gr.Text(label="path checkpoint :")
txt_path_checkpoint_small = gr.Text(label="path output :")
txt_info_reduse = gr.Text(label="info", value="")
reduse_button = gr.Button("reduse")
reduse_button.click(
fn=extract_and_save_ema_model,
inputs=[txt_path_checkpoint, txt_path_checkpoint_small],
outputs=[txt_info_reduse],
)
with gr.TabItem("vocab check experiment"):
check_button = gr.Button("check vocab")
txt_info_check = gr.Text(label="info", value="")
check_button.click(fn=vocab_check, inputs=[project_name], outputs=[txt_info_check])
with gr.TabItem("test model"):
exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
nfe_step = gr.Number(label="n_step", value=32)
file_checkpoint_pt = gr.Textbox(label="Checkpoint", value="")
random_sample_infer = gr.Button("random sample")
ref_text = gr.Textbox(label="ref text")
ref_audio = gr.Audio(label="audio ref", type="filepath")
gen_text = gr.Textbox(label="gen text")
random_sample_infer.click(
fn=get_random_sample_infer, inputs=[project_name], outputs=[ref_text, gen_text, ref_audio]
)
check_button_infer = gr.Button("infer")
gen_audio = gr.Audio(label="audio gen", type="filepath")
check_button_infer.click(
fn=infer,
inputs=[file_checkpoint_pt, exp_name, ref_text, ref_audio, gen_text, nfe_step],
outputs=[gen_audio],
)
@click.command()
@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
@click.option("--host", "-H", default=None, help="Host to run the app on")
@click.option(
"--share",
"-s",
default=False,
is_flag=True,
help="Share the app via Gradio share link",
)
@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
def main(port, host, share, api):
global app
print("Starting app...")
app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
if __name__ == "__main__":
main()
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