# Import 'spaces' early to prevent CUDA initialization conflicts
try:
import spaces
USING_SPACES = True
except ImportError:
USING_SPACES = False
# Delay PyTorch and related imports until after 'spaces'
import re
import gradio as gr
import numpy as np
import tempfile
from einops import rearrange
from pydub import AudioSegment, silence
from model import UNetT, DiT
from cached_path import cached_path
from model.utils import (
get_tokenizer,
convert_char_to_pinyin,
)
from infer.utils_infer import (
load_vocoder,
load_model,
# preprocess_ref_audio_text,
# infer_process,
remove_silence_edges,
remove_silence_for_generated_wav,
save_spectrogram,
)
from tokenizers import Tokenizer
from phonemizer import phonemize
from transformers import pipeline
import click
import soundfile as sf
# Import PyTorch and torchaudio after 'spaces'
import torch
import torchaudio
# GPU decorator for 'spaces'
def gpu_decorator(func):
if USING_SPACES:
return spaces.GPU(func)
else:
return func
# Determine the device
device = (
"cuda"
if torch.cuda.is_available()
else "mps" if torch.backends.mps.is_available() else "cpu"
)
# Set dtype: float16 for GPU, bfloat16 for CPU, and default to float32 for other cases
if device == "cuda":
dtype = torch.float16
elif device == "cpu":
dtype = torch.float32
else:
dtype = torch.float32
# Create the torch.device object
device = torch.device(device)
print(f"Using device: {device}, dtype: {dtype}")
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large-v3-turbo",
torch_dtype=dtype,
device=device,
)
#vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
vocos = load_vocoder()
# --------------------- Settings -------------------- #
target_sample_rate = 24000
n_mel_channels = 100
hop_length = 256
target_rms = 0.1
nfe_step = 16 # 16, 32
cfg_strength = 2.0
ode_method = "euler"
sway_sampling_coef = -1.0
speed = 1
fix_duration = None
DEFAULT_TTS_MODEL = "F5-TTS"
tts_model_choice = DEFAULT_TTS_MODEL
# load models
def load_f5tts(ckpt_path=str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))):
F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
return load_model(DiT, F5TTS_model_cfg, ckpt_path)
def load_e2tts(ckpt_path=str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))):
E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
return load_model(UNetT, E2TTS_model_cfg, ckpt_path)
def load_custom(ckpt_path: str, vocab_path="", model_cfg=None):
ckpt_path, vocab_path = ckpt_path.strip(), vocab_path.strip()
if ckpt_path.startswith("hf://"):
ckpt_path = str(cached_path(ckpt_path))
if vocab_path.startswith("hf://"):
vocab_path = str(cached_path(vocab_path))
if model_cfg is None:
model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path)
#F2TTS_ema_model3 = load_f5tts()
#E2TTS_ema_model4 = load_e2tts() if USING_SPACES else None
custom_ema_model, pre_custom_path = None, ""
chat_model_state = None
chat_tokenizer_state = None
# load models
F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
#F5TTS_ema_model = load_custom(
# "hf://Gregniuki/F5-tts_English_German_Polish/English/model_222600.pt", "", F5TTS_model_cfg
#)
F5TTS_ema_model = load_custom(
"hf://Gregniuki/F5-tts_English_German_Polish/multi/model_300000.pt", "", F5TTS_model_cfg
)
#E2TTS_ema_model2 = load_custom(
# "hf://Gregniuki/F5-tts_English_German_Polish/Polish/model_500000.pt", "", F5TTS_model_cfg
#)
def chunk_text(text, max_chars):
"""
Splits the input text into chunks, ensuring:
- Chunks are split by punctuation where possible.
- If no punctuation is found and the chunk exceeds `split_after_space_chars`,
it is split into smaller chunks of up to `split_after_space_chars`.
Args:
text (str): The text to be split.
max_chars (int): The maximum number of characters per chunk after punctuation.
split_after_space_chars (int): The maximum number of characters per chunk when no punctuation is present.
Returns:
List[str]: A list of text chunks.
"""
if max_chars > 135:
max_chars = 135
if max_chars < 50:
max_chars = 50
split_after_space_chars = max_chars + int(max_chars * 0.33)
chunks = []
current_chunk = ""
# Split the text into sentences based on punctuation followed by whitespace
sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)
for sentence in sentences:
# If adding this sentence doesn't exceed max_chars, append it to the current chunk
if len(current_chunk) + len(sentence) + 1 <= max_chars: # +1 for space
current_chunk += sentence + " "
else:
# If current chunk exceeds split_after_space_chars, handle the splitting
while len(current_chunk) > split_after_space_chars:
split_index = current_chunk.rfind(" ", 0, split_after_space_chars)
if split_index == -1: # No spaces to split; force split at 135 characters
split_index = split_after_space_chars
chunks.append(current_chunk[:split_index].strip())
current_chunk = current_chunk[split_index:].strip()
# Add the current chunk to the list and start a new chunk
if current_chunk:
chunks.append(current_chunk.strip())
current_chunk = sentence + " "
# If the remaining chunk exceeds split_after_space_chars, split it further
while len(current_chunk) > split_after_space_chars:
split_index = current_chunk.rfind(" ", 0, split_after_space_chars)
if split_index == -1: # No spaces to split; force split at 135 characters
split_index = split_after_space_chars
chunks.append(current_chunk[:split_index].strip())
current_chunk = current_chunk[split_index:].strip()
# Add any leftover chunk
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
def text_to_ipa(text, language='en-gb'):
try:
ipa_text = phonemize(
text,
language=language,
backend='espeak',
strip=False,
preserve_punctuation=True,
with_stress=True
)
return ipa_text #preserve_case(text, ipa_text)
except Exception as e:
print(f"Error processing text: {text}. Error: {e}")
return None
@gpu_decorator
def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration=0.15, progress=gr.Progress(), language='en-gb'):
if exp_name == "Multi":
ema_model = F5TTS_ema_model
# elif exp_name == "Polish":
# ema_model = E2TTS_ema_model
# elif exp_name == "Deutsch":
# ema_model = E2TTS_ema_model2
#ref_audio, ref_text = preprocess_ref_audio_text(ref_audio, ref_text, show_info=show_info)
audio, sr = ref_audio
if audio.shape[0] > 1:
audio = torch.mean(audio, dim=0, keepdim=True)
rms = torch.sqrt(torch.mean(torch.square(audio)))
if rms < target_rms:
audio = audio * target_rms / rms
if sr != target_sample_rate:
resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
audio = resampler(audio)
audio = audio.to(device)
tokenizer = Tokenizer.from_file("data/Emilia_ZH_EN_pinyin/tokenizer.json")
vocab_size = tokenizer.get_vocab_size()
vocab = tokenizer.get_vocab()
generated_waves = []
spectrograms = []
# Remove the last 5 chats, ensuring you don't attempt to slice beyond the list length
ref_text = ref_text[:-5] + "... " if len(ref_text) >= 5 else ref_text
# Ensure ref_text ends with a space if the last character is single-byte
# if len(ref_text[-1].encode("utf-8")) == 1:
# ref_text = ref_text + ". ."
# Define weights for characters
punctuation_weights = {",": 0, ".": 0, " ": 0} # Add more punctuation as needed
for i, gen_text in enumerate(progress.tqdm(gen_text_batches)):
# Prepare the text
ipa_text_ref = text_to_ipa(ref_text, language=language)
ipa_text_gen = text_to_ipa(gen_text, language=language)
print(ipa_text_gen)
text_list = [ref_text_ref + gen_text_gen]
encoding = tokenizer.encode(text_list)
tokens = encoding.tokens
text_list = ' '.join(map(str, tokens))
final_text_list = [text_list]
print(final_text_list)
# Calculate reference audio length
ref_audio_len = audio.shape[-1] // hop_length
if fix_duration is not None:
duration = int(fix_duration * target_sample_rate / hop_length)
else:
# Calculate text lengths with weights
def calculate_weighted_length(text):
length = len(text.encode("utf-8"))
additional_length = sum(punctuation_weights.get(char, 0) for char in text)
return length + additional_length
ref_text_len = calculate_weighted_length(ref_text)
gen_text_len = calculate_weighted_length(gen_text)
# Duration calculation considering global speed factor
# duration = int(ref_audio_len) + int(((ref_audio_len / ref_text_len) * gen_text_len) / speed)
duration = max(250, int(ref_audio_len) + int(((ref_audio_len / ref_text_len) * gen_text_len) / speed))
# Print the calculated duration
print(f"Chunk {i + 1}: Duration: {duration} speed {speed}")
# inference
with torch.inference_mode():
# Ensure all inputs are on the same device as ema_model
audio = audio.to(ema_model.device) # Match ema_model's device
final_text_list = [t.to(ema_model.device) if isinstance(t, torch.Tensor) else t for t in final_text_list]
generated, _ = ema_model.sample(
cond=audio,
text=final_text_list,
duration=duration,
steps=nfe_step,
cfg_strength=cfg_strength,
sway_sampling_coef=sway_sampling_coef,
)
# Process generated tensor
generated = generated[:, ref_audio_len:, :]
generated_mel_spec = rearrange(generated, "1 n d -> 1 d n")
# Convert to appropriate dtype and device
# generated_mel_spec = generated_mel_spec.to(dtype=torch.float16, device=vocos.device) # Ensure device matches vocos
generated_wave = vocos.decode(generated_mel_spec)
# Adjust wave RMS if needed
if rms < target_rms:
generated_wave = generated_wave * rms / target_rms
# Convert to numpy
generated_wave = generated_wave.squeeze().cpu().numpy()
# Append to list
generated_waves.append(generated_wave)
# spectrograms.append(generated_mel_spec[0].cpu().numpy())
# Ensure generated_mel_spec is in a compatible dtype (e.g., float32) before passing it to numpy
# generated_mel_spec = generated_mel_spec.to(dtype=torch.float32) # Convert to float32 if it's in bfloat16
# Proceed with the rest of your operations
spectrograms.append(generated_mel_spec[0].cpu().numpy())
# Combine all generated waves with cross-fading
if cross_fade_duration <= 0:
# Simply concatenate
final_wave = np.concatenate(generated_waves)
else:
final_wave = generated_waves[0]
for i in range(1, len(generated_waves)):
prev_wave = final_wave
next_wave = generated_waves[i]
# Calculate cross-fade samples, ensuring it does not exceed wave lengths
cross_fade_samples = int(cross_fade_duration * target_sample_rate)
cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
if cross_fade_samples <= 0:
# No overlap possible, concatenate
final_wave = np.concatenate([prev_wave, next_wave])
continue
# Overlapping parts
prev_overlap = prev_wave[-cross_fade_samples:]
next_overlap = next_wave[:cross_fade_samples]
# Fade out and fade in
fade_out = np.linspace(1, 0, cross_fade_samples)
fade_in = np.linspace(0, 1, cross_fade_samples)
# Cross-faded overlap
cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
# Combine
new_wave = np.concatenate([
prev_wave[:-cross_fade_samples],
cross_faded_overlap,
next_wave[cross_fade_samples:]
])
final_wave = new_wave
# Remove silence
if remove_silence:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
# Convert to float32 before writing
final_wave_float32 = final_wave.astype(np.float32)
sf.write(f.name, final_wave_float32, target_sample_rate)
aseg = AudioSegment.from_file(f.name)
non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
non_silent_wave = AudioSegment.silent(duration=0)
for non_silent_seg in non_silent_segs:
non_silent_wave += non_silent_seg
aseg = non_silent_wave
aseg.export(f.name, format="wav")
final_wave, _ = torchaudio.load(f.name)
final_wave = final_wave.squeeze().cpu().numpy()
# Create a combined spectrogram
combined_spectrogram = np.concatenate(spectrograms, axis=1)
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
spectrogram_path = tmp_spectrogram.name
save_spectrogram(combined_spectrogram, spectrogram_path)
return (target_sample_rate, final_wave), spectrogram_path
@gpu_decorator
def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fade_duration=0.15, language='en-gb' # Set the desired language code dynamically
):
print(gen_text)
gr.Info("Converting audio...")
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
aseg = AudioSegment.from_file(ref_audio_orig)
aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=150)
non_silent_segs = silence.split_on_silence(
aseg, min_silence_len=700, silence_thresh=-50, keep_silence=700
)
non_silent_wave = AudioSegment.silent(duration=0)
for non_silent_seg in non_silent_segs:
non_silent_wave += non_silent_seg
aseg = non_silent_wave
audio_duration = len(aseg)
if audio_duration > 10000:
gr.Warning("Audio is over 10s, clipping to only first 10s.")
aseg = aseg[:10000]
aseg.export(f.name, format="wav")
ref_audio = f.name
if not ref_text.strip():
gr.Info("No reference text provided, transcribing reference audio...")
ref_text = pipe(
ref_audio,
chunk_length_s=15,
batch_size=128,
generate_kwargs={"task": "transcribe"# ,"language": language # Use the variable here
},
return_timestamps=False,
)["text"].strip()
gr.Info("Finished transcription")
else:
gr.Info("Using custom reference text...")
# Add the functionality to ensure it ends with ". "
if not ref_text.endswith(". "):
if ref_text.endswith("."):
ref_text += " "
else:
ref_text += ". "
audio, sr = torchaudio.load(ref_audio)
# Use the new chunk_text function to split gen_text
max_chars = int(speed * 0.5 * (len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (20 - audio.shape[-1] / sr )))
print(f"text: {max_chars} ")
gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
print('ref_text', ref_text)
for i, batch_text in enumerate(gen_text_batches):
print(f'gen_text {i}', batch_text)
gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches")
return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration, language)
@gpu_decorator
def generate_podcast(script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, exp_name, remove_silence):
# Split the script into speaker blocks
speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE)
speaker_blocks = speaker_pattern.split(script)[1:] # Skip the first empty element
generated_audio_segments = []
for i in range(0, len(speaker_blocks), 2):
speaker = speaker_blocks[i]
text = speaker_blocks[i+1].strip()
# Determine which speaker is talking
if speaker == speaker1_name:
ref_audio = ref_audio1
ref_text = ref_text1
elif speaker == speaker2_name:
ref_audio = ref_audio2
ref_text = ref_text2
else:
continue # Skip if the speaker is neither speaker1 nor speaker2
# Generate audio for this block
audio, _ = infer(ref_audio, ref_text, text, exp_name, remove_silence, language)
# Convert the generated audio to a numpy array
sr, audio_data = audio
# Save the audio data as a WAV file
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
sf.write(temp_file.name, audio_data, sr)
audio_segment = AudioSegment.from_wav(temp_file.name)
generated_audio_segments.append(audio_segment)
# Add a short pause between speakers
pause = AudioSegment.silent(duration=500) # 500ms pause
generated_audio_segments.append(pause)
# Concatenate all audio segments
final_podcast = sum(generated_audio_segments)
# Export the final podcast
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
podcast_path = temp_file.name
final_podcast.export(podcast_path, format="wav")
return podcast_path
def parse_speechtypes_text(gen_text):
# Pattern to find (Emotion)
pattern = r'\((.*?)\)'
# Split the text by the pattern
tokens = re.split(pattern, gen_text)
segments = []
current_emotion = 'Regular'
for i in range(len(tokens)):
if i % 2 == 0:
# This is text
text = tokens[i].strip()
if text:
segments.append({'emotion': current_emotion, 'text': text})
else:
# This is emotion
emotion = tokens[i].strip()
current_emotion = emotion
return segments
def update_speed(new_speed):
global speed
speed = new_speed
return f"Speed set to: {speed}"
with gr.Blocks() as app_credits:
gr.Markdown("""
# Credits
* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation
* [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation
""")
with gr.Blocks() as app_tts:
gr.Markdown("# Batched TTS")
ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
model_choice = gr.Radio(
choices=["Multi"], label="Choose TTS Model", value="Multi"
)
language_choice = gr.Dropdown(
choices=["pl", "de", "en", "en-us", "en-gb", "uk", "ru"], label="Choose Language", value="en"
)
generate_btn = gr.Button("Synthesize", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
ref_text_input = gr.Textbox(
label="Reference Text",
info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.",
lines=2,
)
remove_silence = gr.Checkbox(
label="Remove Silences",
info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.",
value=False,
)
speed_slider = gr.Slider(
label="Speed",
minimum=0.3,
maximum=2.0,
value=1.0, # Assuming a default speed value
step=0.1,
info="Adjust the speed of the audio.",
)
cross_fade_duration_slider = gr.Slider(
label="Cross-Fade Duration (s)",
minimum=0.0,
maximum=1.0,
value=0.15,
step=0.01,
info="Set the duration of the cross-fade between audio clips.",
)
speed_slider.change(update_speed, inputs=speed_slider)
audio_output = gr.Audio(label="Synthesized Audio")
spectrogram_output = gr.Image(label="Spectrogram")
generate_btn.click(
infer,
inputs=[
ref_audio_input,
ref_text_input,
gen_text_input,
model_choice,
remove_silence,
cross_fade_duration_slider,
language_choice,
],
outputs=[audio_output, spectrogram_output],
)
def parse_emotional_text(gen_text):
# Pattern to find (Emotion)
pattern = r'\((.*?)\)'
# Split the text by the pattern
tokens = re.split(pattern, gen_text)
segments = []
current_emotion = 'Regular'
for i in range(len(tokens)):
if i % 2 == 0:
# This is text
text = tokens[i].strip()
if text:
segments.append({'emotion': current_emotion, 'text': text})
else:
# This is emotion
emotion = tokens[i].strip()
current_emotion = emotion
return segments
with gr.Blocks() as app_emotional:
# New section for emotional generation
gr.Markdown(
"""
# Multiple Speech-Type Generation
This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified.
**Example Input:**
(Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?!
"""
)
gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.")
# Regular speech type (mandatory)
with gr.Row():
regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False)
regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath')
regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2)
# Additional speech types (up to 9 more)
max_speech_types = 10
speech_type_names = []
speech_type_audios = []
speech_type_ref_texts = []
speech_type_delete_btns = []
for i in range(max_speech_types - 1):
with gr.Row():
name_input = gr.Textbox(label='Speech Type Name', visible=False)
audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False)
ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False)
delete_btn = gr.Button("Delete", variant="secondary", visible=False)
speech_type_names.append(name_input)
speech_type_audios.append(audio_input)
speech_type_ref_texts.append(ref_text_input)
speech_type_delete_btns.append(delete_btn)
# Button to add speech type
add_speech_type_btn = gr.Button("Add Speech Type")
# Keep track of current number of speech types
speech_type_count = gr.State(value=0)
# Function to add a speech type
def add_speech_type_fn(speech_type_count):
if speech_type_count < max_speech_types - 1:
speech_type_count += 1
# Prepare updates for the components
name_updates = []
audio_updates = []
ref_text_updates = []
delete_btn_updates = []
for i in range(max_speech_types - 1):
if i < speech_type_count:
name_updates.append(gr.update(visible=True))
audio_updates.append(gr.update(visible=True))
ref_text_updates.append(gr.update(visible=True))
delete_btn_updates.append(gr.update(visible=True))
else:
name_updates.append(gr.update())
audio_updates.append(gr.update())
ref_text_updates.append(gr.update())
delete_btn_updates.append(gr.update())
else:
# Optionally, show a warning
# gr.Warning("Maximum number of speech types reached.")
name_updates = [gr.update() for _ in range(max_speech_types - 1)]
audio_updates = [gr.update() for _ in range(max_speech_types - 1)]
ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)]
delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)]
return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
add_speech_type_btn.click(
add_speech_type_fn,
inputs=speech_type_count,
outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
)
# Function to delete a speech type
def make_delete_speech_type_fn(index):
def delete_speech_type_fn(speech_type_count):
# Prepare updates
name_updates = []
audio_updates = []
ref_text_updates = []
delete_btn_updates = []
for i in range(max_speech_types - 1):
if i == index:
name_updates.append(gr.update(visible=False, value=''))
audio_updates.append(gr.update(visible=False, value=None))
ref_text_updates.append(gr.update(visible=False, value=''))
delete_btn_updates.append(gr.update(visible=False))
else:
name_updates.append(gr.update())
audio_updates.append(gr.update())
ref_text_updates.append(gr.update())
delete_btn_updates.append(gr.update())
speech_type_count = max(0, speech_type_count - 1)
return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates
return delete_speech_type_fn
for i, delete_btn in enumerate(speech_type_delete_btns):
delete_fn = make_delete_speech_type_fn(i)
delete_btn.click(
delete_fn,
inputs=speech_type_count,
outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns
)
# Text input for the prompt
gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10)
# Model choice
model_choice_emotional = gr.Radio(
choices=["Multi"], label="Choose TTS Model", value="Multi"
)
with gr.Accordion("Advanced Settings", open=False):
remove_silence_emotional = gr.Checkbox(
label="Remove Silences",
value=True,
)
# Generate button
generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary")
# Output audio
audio_output_emotional = gr.Audio(label="Synthesized Audio")
@gpu_decorator
def generate_emotional_speech(
regular_audio,
regular_ref_text,
gen_text,
*args,
):
num_additional_speech_types = max_speech_types - 1
speech_type_names_list = args[:num_additional_speech_types]
speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types]
speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types]
model_choice = args[3 * num_additional_speech_types]
remove_silence = args[3 * num_additional_speech_types + 1]
# Collect the speech types and their audios into a dict
speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}}
for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list):
if name_input and audio_input:
speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input}
# Parse the gen_text into segments
segments = parse_speechtypes_text(gen_text)
# For each segment, generate speech
generated_audio_segments = []
current_emotion = 'Regular'
for segment in segments:
emotion = segment['emotion']
text = segment['text']
if emotion in speech_types:
current_emotion = emotion
else:
# If emotion not available, default to Regular
current_emotion = 'Regular'
ref_audio = speech_types[current_emotion]['audio']
ref_text = speech_types[current_emotion].get('ref_text', '')
# Generate speech for this segment
audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, language)
sr, audio_data = audio
# generated_audio_segments.append(audio_data)
# Ensure audio_data is float32
#audio_data = audio_data.astype(np.float32)
generated_audio_segments.append(audio_data)
# Concatenate all audio segments
if generated_audio_segments:
final_audio_data = np.concatenate(generated_audio_segments)#.astype(np.float32)
return (sr, final_audio_data)
else:
gr.Warning("No audio generated.")
return None
generate_emotional_btn.click(
generate_emotional_speech,
inputs=[
regular_audio,
regular_ref_text,
gen_text_input_emotional,
] + speech_type_names + speech_type_audios + speech_type_ref_texts + [
model_choice_emotional,
remove_silence_emotional,
],
outputs=audio_output_emotional,
)
# Validation function to disable Generate button if speech types are missing
def validate_speech_types(
gen_text,
regular_name,
*args
):
num_additional_speech_types = max_speech_types - 1
speech_type_names_list = args[:num_additional_speech_types]
# Collect the speech types names
speech_types_available = set()
if regular_name:
speech_types_available.add(regular_name)
for name_input in speech_type_names_list:
if name_input:
speech_types_available.add(name_input)
# Parse the gen_text to get the speech types used
segments = parse_emotional_text(gen_text)
speech_types_in_text = set(segment['emotion'] for segment in segments)
# Check if all speech types in text are available
missing_speech_types = speech_types_in_text - speech_types_available
if missing_speech_types:
# Disable the generate button
return gr.update(interactive=False)
else:
# Enable the generate button
return gr.update(interactive=True)
gen_text_input_emotional.change(
validate_speech_types,
inputs=[gen_text_input_emotional, regular_name] + speech_type_names,
outputs=generate_emotional_btn
)
with gr.Blocks() as app:
gr.Markdown(
"""
# F5 TTS
This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
The checkpoint support Polish English and German.
Generations using CPU takes usually 2-3 minutes using 8 step inferece.
If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 5s, and shortening your prompt.
**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
"""
)
gr.HTML(
"""
<a href="https://www.buymeacoffee.com/gregniuki" target="_blank">
<img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;">
</a>
"""
)
gr.TabbedInterface([app_tts, app_emotional, app_credits], ["TTS", "Multi-Style", "Credits"])
@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(f"Starting app...")
app.queue(api_open=api).launch(
server_name=host, server_port=port, share=share, show_api=api
)
if __name__ == "__main__":
main()