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fix infer-gradio chat feature etc.

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SWivid
2025-05-04 08:00:16 +08:00
parent b0658bfd24
commit f00ac4d06b
1 changed files with 176 additions and 337 deletions
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513
src/f5_tts/infer/infer_gradio.py
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@@ -90,7 +90,7 @@ chat_tokenizer_state = None
@gpu_decorator
def generate_response(messages, model, tokenizer):
def chat_model_inference(messages, model, tokenizer):
"""Generate response using Qwen"""
text = tokenizer.apply_chat_template(
messages,
@@ -112,21 +112,21 @@ def generate_response(messages, model, tokenizer):
return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
def read_text_file(file_path):
"""Read content from a .txt file"""
if file_path:
with open(file_path, "r", encoding="utf-8") as f:
return f.read().strip()
return ""
@gpu_decorator
def load_text_from_file(file):
if file:
with open(file, "r", encoding="utf-8") as f:
text = f.read().strip()
else:
text = ""
return gr.update(value=text)
@gpu_decorator
def infer(
ref_audio_orig,
ref_text,
ref_text_file,
gen_text,
gen_text_file,
model,
remove_silence,
seed,
@@ -139,20 +139,13 @@ def infer(
gr.Warning("Please provide reference audio.")
return gr.update(), gr.update(), ref_text
# Use text from file if provided, otherwise use direct text input
ref_text = read_text_file(ref_text_file) or ref_text
gen_text = read_text_file(gen_text_file) or gen_text
# Set inference seed
torch.manual_seed(seed)
if not gen_text.strip():
gr.Warning("Please enter text to generate or upload a text file.")
return gr.update(), gr.update(), ref_text
# Set random seed for reproducibility
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=show_info)
if model == DEFAULT_TTS_MODEL:
@@ -212,36 +205,40 @@ with gr.Blocks() as app_credits:
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)
with gr.Column(scale=1):
gen_text_file = gr.File(label="Upload Text File to Generate (.txt)", file_types=[".txt"])
with gr.Row(equal_height=True):
gen_text_input = gr.Textbox(
label="Text to Generate",
lines=10,
max_lines=40,
scale=4,
)
gen_text_file = gr.File(label="Load Text to Generate from File (.txt)", file_types=[".txt"], scale=1)
generate_btn = gr.Button("Synthesize", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
with gr.Row(equal_height=True):
ref_text_input = gr.Textbox(
label="Reference Text",
info="Leave blank to automatically transcribe the reference audio. If you enter text or upload a file, it will override automatic transcription.",
lines=2,
scale=4,
)
ref_text_file = gr.File(
label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1, height=1
)
with gr.Column(scale=1):
ref_text_file = gr.File(label="Upload Reference Text File (.txt)", file_types=[".txt"])
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,
)
with gr.Row():
randomize_seed = gr.Checkbox(
label="Randomize Seed",
info="Check to use a random seed for each generation. Uncheck to use the seed specified.",
value=True,
info="Check to use a random seed for each generation. Uncheck to use the seed specified below.",
)
seed_input = gr.Textbox(
label="Seed",
value="0",
placeholder="Enter a seed value",
scale=1,
scale=3,
)
seed_input = gr.Number(show_label=False, value=0, precision=0, scale=1)
with gr.Column(scale=4):
remove_silence = gr.Checkbox(
label="Remove Silences",
info="If undesired long silence(s) produced, turn on to automatically detect and crop.",
value=False,
)
speed_slider = gr.Slider(
label="Speed",
minimum=0.3,
@@ -253,9 +250,9 @@ with gr.Blocks() as app_tts:
nfe_slider = gr.Slider(
label="NFE Steps",
minimum=4,
maximum=71,
maximum=64,
value=32,
step=1,
step=2,
info="Set the number of denoising steps.",
)
cross_fade_duration_slider = gr.Slider(
@@ -270,25 +267,11 @@ with gr.Blocks() as app_tts:
audio_output = gr.Audio(label="Synthesized Audio")
spectrogram_output = gr.Image(label="Spectrogram")
@gpu_decorator
def update_gen_text_from_file(file):
"""Update the generate text input when a .txt file is uploaded"""
text = read_text_file(file)
return gr.update(value=text)
@gpu_decorator
def update_ref_text_from_file(file):
"""Update the reference text input when a .txt file is uploaded"""
text = read_text_file(file)
return gr.update(value=text)
@gpu_decorator
def basic_tts(
ref_audio_input,
ref_text_input,
ref_text_file,
gen_text_input,
gen_text_file,
remove_silence,
randomize_seed,
seed_input,
@@ -298,23 +281,17 @@ with gr.Blocks() as app_tts:
):
# Determine the seed to use
if randomize_seed:
seed = np.random.randint(0, 2**31)
seed = np.random.randint(0, 2**31 - 1)
else:
try:
seed = int(seed_input)
if seed < 0:
gr.Warning("Seed must be a non-negative integer. Using random seed instead.")
seed = np.random.randint(0, 2**31)
except ValueError:
gr.Warning("Invalid seed value. Using random seed instead.")
seed = np.random.randint(0, 2**31)
seed = seed_input
if seed < 0 or seed > 2**31 - 1:
gr.Warning("Seed must in range 0 ~ 2147483647. Using random seed instead.")
seed = np.random.randint(0, 2**31 - 1)
audio_out, spectrogram_path, ref_text_out = infer(
ref_audio_input,
ref_text_input,
ref_text_file,
gen_text_input,
gen_text_file,
tts_model_choice,
remove_silence,
seed=seed,
@@ -322,16 +299,16 @@ with gr.Blocks() as app_tts:
nfe_step=nfe_slider,
speed=speed_slider,
)
return audio_out, spectrogram_path, ref_text_out, str(seed)
return audio_out, spectrogram_path, ref_text_out, seed
gen_text_file.change(
update_gen_text_from_file,
gen_text_file.upload(
load_text_from_file,
inputs=[gen_text_file],
outputs=[gen_text_input],
)
ref_text_file.change(
update_ref_text_from_file,
ref_text_file.upload(
load_text_from_file,
inputs=[ref_text_file],
outputs=[ref_text_input],
)
@@ -341,9 +318,7 @@ with gr.Blocks() as app_tts:
inputs=[
ref_audio_input,
ref_text_input,
ref_text_file,
gen_text_input,
gen_text_file,
remove_silence,
randomize_seed,
seed_input,
@@ -419,14 +394,14 @@ with gr.Blocks() as app_multistyle:
# Regular speech type (mandatory)
with gr.Row() as regular_row:
with gr.Column():
with gr.Column(scale=1, min_width=160):
regular_name = gr.Textbox(value="Regular", label="Speech Type Name")
regular_insert = gr.Button("Insert Label", variant="secondary")
regular_audio = gr.Audio(label="Regular Reference Audio", type="filepath")
with gr.Row():
regular_ref_text = gr.Textbox(label="Reference Text (Regular)", lines=2)
with gr.Column(scale=1):
regular_ref_text_file = gr.File(label="Upload Reference Text File (.txt)", file_types=[".txt"])
with gr.Column(scale=3):
regular_audio = gr.Audio(label="Regular Reference Audio", type="filepath")
with gr.Row(scale=4, equal_height=True):
regular_ref_text = gr.Textbox(label="Reference Text (Regular)", lines=8, scale=3)
regular_ref_text_file = gr.File(label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1)
# Regular speech type (max 100)
max_speech_types = 100
@@ -441,15 +416,17 @@ with gr.Blocks() as app_multistyle:
# Additional speech types (99 more)
for i in range(max_speech_types - 1):
with gr.Row(visible=False) as row:
with gr.Column():
with gr.Column(scale=1, min_width=160):
name_input = gr.Textbox(label="Speech Type Name")
delete_btn = gr.Button("Delete Type", variant="secondary")
insert_btn = gr.Button("Insert Label", variant="secondary")
audio_input = gr.Audio(label="Reference Audio", type="filepath")
with gr.Row():
ref_text_input = gr.Textbox(label="Reference Text", lines=2)
with gr.Column(scale=1):
ref_text_file_input = gr.File(label="Upload Reference Text File (.txt)", file_types=[".txt"])
with gr.Column(scale=3):
audio_input = gr.Audio(label="Reference Audio", type="filepath")
with gr.Row(scale=4, equal_height=True):
ref_text_input = gr.Textbox(label="Reference Text", lines=8, scale=3)
ref_text_file_input = gr.File(
label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1
)
speech_type_rows.append(row)
speech_type_names.append(name_input)
speech_type_audios.append(audio_input)
@@ -481,13 +458,6 @@ with gr.Blocks() as app_multistyle:
def delete_speech_type_fn():
return gr.update(visible=False), None, None, None, None
# Function to update reference text from file
@gpu_decorator
def update_ref_text_from_file(file):
"""Update the reference text input when a .txt file is uploaded"""
text = read_text_file(file)
return gr.update(value=text)
# Update delete button clicks and ref text file changes
for i in range(1, len(speech_type_delete_btns)):
speech_type_delete_btns[i].click(
@@ -500,27 +470,29 @@ with gr.Blocks() as app_multistyle:
speech_type_ref_text_files[i],
],
)
speech_type_ref_text_files[i].change(
update_ref_text_from_file,
speech_type_ref_text_files[i].upload(
load_text_from_file,
inputs=[speech_type_ref_text_files[i]],
outputs=[speech_type_ref_texts[i]],
)
# Update regular speech type ref text file
regular_ref_text_file.change(
update_ref_text_from_file,
regular_ref_text_file.upload(
load_text_from_file,
inputs=[regular_ref_text_file],
outputs=[regular_ref_text],
)
# Text input for the prompt
gen_text_input_multistyle = gr.Textbox(
label="Text to Generate",
lines=10,
placeholder="Enter the script with speaker names (or emotion types) at the start of each block, e.g.:\n\n{Regular} Hello, I'd like to order a sandwich please.\n{Surprised} What do you mean you're out of bread?\n{Sad} I really wanted a sandwich though...\n{Angry} You know what, darn you and your little shop!\n{Whisper} I'll just go back home and cry now.\n{Shouting} Why me?!",
)
with gr.Column(scale=1):
gen_text_file_multistyle = gr.File(label="Upload Text File to Generate (.txt)", file_types=[".txt"])
with gr.Row(equal_height=True):
gen_text_input_multistyle = gr.Textbox(
label="Text to Generate",
lines=10,
max_lines=40,
scale=4,
placeholder="Enter the script with speaker names (or emotion types) at the start of each block, e.g.:\n\n{Regular} Hello, I'd like to order a sandwich please.\n{Surprised} What do you mean you're out of bread?\n{Sad} I really wanted a sandwich though...\n{Angry} You know what, darn you and your little shop!\n{Whisper} I'll just go back home and cry now.\n{Shouting} Why me?!",
)
gen_text_file_multistyle = gr.File(label="Load Text to Generate from File (.txt)", file_types=[".txt"], scale=1)
def make_insert_speech_type_fn(index):
def insert_speech_type_fn(current_text, speech_type_name):
@@ -542,20 +514,9 @@ with gr.Blocks() as app_multistyle:
with gr.Accordion("Advanced Settings", open=False):
remove_silence_multistyle = gr.Checkbox(
label="Remove Silences",
info="Turn on to automatically detect and crop long silences.",
value=True,
)
with gr.Row():
randomize_seed_multistyle = gr.Checkbox(
label="Randomize Seed",
value=True,
info="Check to use a random seed for each generation. Uncheck to use the seed specified below.",
)
seed_input_multistyle = gr.Textbox(
label="Seed",
value="0",
placeholder="Enter a seed value",
scale=1,
)
# Generate button
generate_multistyle_btn = gr.Button("Generate Multi-Style Speech", variant="primary")
@@ -563,60 +524,30 @@ with gr.Blocks() as app_multistyle:
# Output audio
audio_output_multistyle = gr.Audio(label="Synthesized Audio")
@gpu_decorator
def update_gen_text_from_file(file):
"""Update the generate text input when a .txt file is uploaded"""
text = read_text_file(file)
return gr.update(value=text)
gen_text_file_multistyle.change(
fn=lambda file, text, regular, *names: (
update_gen_text_from_file(file),
validate_speech_types(text, file, regular, *names),
),
inputs=[gen_text_file_multistyle, gen_text_input_multistyle, regular_name] + speech_type_names,
outputs=[gen_text_input_multistyle, generate_multistyle_btn],
gen_text_file_multistyle.upload(
load_text_from_file,
inputs=[gen_text_file_multistyle],
outputs=[gen_text_input_multistyle],
)
@gpu_decorator
def generate_multistyle_speech(
gen_text,
gen_text_file,
randomize_seed,
seed_input,
*args,
):
# Determine the seed to use
if randomize_seed:
seed = np.random.randint(0, 2**31)
else:
try:
seed = int(seed_input)
if seed < 0:
gr.Warning("Seed must be a non-negative integer. Using random seed instead.")
seed = np.random.randint(0, 2**31)
except ValueError:
gr.Warning("Invalid seed value. Using random seed instead.")
seed = np.random.randint(0, 2**31)
speech_type_names_list = args[:max_speech_types]
speech_type_audios_list = args[max_speech_types : 2 * max_speech_types]
speech_type_ref_texts_list = args[2 * max_speech_types : 3 * max_speech_types]
speech_type_ref_text_files_list = args[3 * max_speech_types : 4 * max_speech_types]
remove_silence = args[4 * max_speech_types]
remove_silence = args[3 * max_speech_types]
# Collect the speech types and their audios into a dict
speech_types = OrderedDict()
# Use text from file if provided, otherwise use direct text input
gen_text = read_text_file(gen_text_file) or gen_text
ref_text_idx = 0
for name_input, audio_input, ref_text_input, ref_text_file_input in zip(
speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list, speech_type_ref_text_files_list
for name_input, audio_input, ref_text_input in zip(
speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list
):
ref_text = read_text_file(ref_text_file_input) or ref_text_input
if name_input and audio_input:
speech_types[name_input] = {"audio": audio_input, "ref_text": ref_text}
speech_types[name_input] = {"audio": audio_input, "ref_text": ref_text_input}
else:
speech_types[f"@{ref_text_idx}@"] = {"audio": "", "ref_text": ""}
ref_text_idx += 1
@@ -642,12 +573,15 @@ with gr.Blocks() as app_multistyle:
ref_audio = speech_types[current_style]["audio"]
except KeyError:
gr.Warning(f"Please provide reference audio for type {current_style}.")
return [None] + [speech_types[style]["ref_text"] for style in speech_types] + [str(seed)]
return [None] + [speech_types[style]["ref_text"] for style in speech_types]
ref_text = speech_types[current_style].get("ref_text", "")
# TODO. Attribute each type a unique seed (maybe also speed, pseudo-feature for #730 #813)
seed = np.random.randint(0, 2**31 - 1)
# Generate speech for this segment
audio_out, _, ref_text_out = infer(
ref_audio, ref_text, None, text, None, tts_model_choice, remove_silence, seed, 0, show_info=print
ref_audio, ref_text, text, tts_model_choice, remove_silence, seed, 0, show_info=print
) # show_info=print no pull to top when generating
sr, audio_data = audio_out
@@ -657,29 +591,29 @@ with gr.Blocks() as app_multistyle:
# Concatenate all audio segments
if generated_audio_segments:
final_audio_data = np.concatenate(generated_audio_segments)
return [(sr, final_audio_data)] + [speech_types[style]["ref_text"] for style in speech_types] + [str(seed)]
return [(sr, final_audio_data)] + [speech_types[style]["ref_text"] for style in speech_types]
else:
gr.Warning("No audio generated.")
return [None] + [speech_types[style]["ref_text"] for style in speech_types] + [str(seed)]
return [None] + [speech_types[style]["ref_text"] for style in speech_types]
generate_multistyle_btn.click(
generate_multistyle_speech,
inputs=[gen_text_input_multistyle, gen_text_file_multistyle, randomize_seed_multistyle, seed_input_multistyle]
inputs=[
gen_text_input_multistyle,
]
+ speech_type_names
+ speech_type_audios
+ speech_type_ref_texts
+ speech_type_ref_text_files
+ [remove_silence_multistyle],
outputs=[audio_output_multistyle] + speech_type_ref_texts + [seed_input_multistyle],
+ [
remove_silence_multistyle,
],
outputs=[audio_output_multistyle] + speech_type_ref_texts,
)
# Validation function to disable Generate button if speech types are missing
def validate_speech_types(gen_text, gen_text_file, regular_name, *args):
def validate_speech_types(gen_text, regular_name, *args):
speech_type_names_list = args
# Use text from file if provided, otherwise use direct text input
gen_text = read_text_file(gen_text_file) or gen_text
# Collect the speech types names
speech_types_available = set()
if regular_name:
@@ -704,19 +638,10 @@ with gr.Blocks() as app_multistyle:
gen_text_input_multistyle.change(
validate_speech_types,
inputs=[gen_text_input_multistyle, gen_text_file_multistyle, regular_name] + speech_type_names,
inputs=[gen_text_input_multistyle, regular_name] + speech_type_names,
outputs=generate_multistyle_btn,
)
gen_text_file_multistyle.change(
fn=lambda file, text, regular, *names: (
update_gen_text_from_file(file),
validate_speech_types(text, file, regular, *names),
),
inputs=[gen_text_file_multistyle, gen_text_input_multistyle, regular_name] + speech_type_names,
outputs=[gen_text_input_multistyle, generate_multistyle_btn],
)
with gr.Blocks() as app_chat:
gr.Markdown(
@@ -781,35 +706,33 @@ Have a conversation with an AI using your reference voice!
ref_audio_chat = gr.Audio(label="Reference Audio", type="filepath")
with gr.Column():
with gr.Accordion("Advanced Settings", open=False):
remove_silence_chat = gr.Checkbox(
label="Remove Silences",
value=True,
)
with gr.Row():
with gr.Row(equal_height=True):
ref_text_chat = gr.Textbox(
label="Reference Text",
info="Optional: Leave blank to auto-transcribe",
lines=2,
scale=3,
)
with gr.Column(scale=1):
ref_text_file_chat = gr.File(label="Upload Reference Text File (.txt)", file_types=[".txt"])
ref_text_file_chat = gr.File(
label="Load Reference Text from File (.txt)", file_types=[".txt"], scale=1
)
with gr.Row():
randomize_seed_chat = gr.Checkbox(
label="Randomize Seed",
value=True,
info="Uncheck to use the seed specified.",
scale=3,
)
seed_input_chat = gr.Number(show_label=False, value=0, precision=0, scale=1)
remove_silence_chat = gr.Checkbox(
label="Remove Silences",
value=True,
)
system_prompt_chat = gr.Textbox(
label="System Prompt",
value="You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
lines=2,
)
with gr.Row():
randomize_seed_chat = gr.Checkbox(
label="Randomize Seed",
value=True,
info="Check to use a random seed for each generation. Uncheck to use the seed specified below.",
)
seed_input_chat = gr.Textbox(
label="Seed",
value="0",
placeholder="Enter a seed value",
scale=1,
)
chatbot_interface = gr.Chatbot(label="Conversation", type="messages")
@@ -825,80 +748,59 @@ Have a conversation with an AI using your reference voice!
label="Type your message",
lines=1,
)
with gr.Column(scale=1):
text_file_chat = gr.File(label="Upload Text File (.txt)", file_types=[".txt"])
send_btn_chat = gr.Button("Send Message")
clear_btn_chat = gr.Button("Clear Conversation")
conversation_state = gr.State(
value=[
{
"role": "system",
"content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
}
]
)
# Modify process_audio_input to use model and tokenizer from state
# Modify process_audio_input to generate user input
@gpu_decorator
def process_audio_input(audio_path, text, text_file, history, conv_state):
"""Handle audio, text, or file input from user"""
if not audio_path and not text.strip() and not text_file:
return history, conv_state, "", None
def process_audio_input(conv_state, audio_path, text):
"""Handle audio or text input from user"""
# Use file input if provided, then direct text input, then audio transcription
if text_file:
text = read_text_file(text_file)
elif audio_path:
if not audio_path and not text.strip():
return conv_state
if audio_path:
text = preprocess_ref_audio_text(audio_path, text)[1]
if not text.strip():
return history, conv_state, "", None
return conv_state
conv_state.append({"role": "user", "content": text})
history.append((text, None))
return conv_state
response = generate_response(conv_state, chat_model_state, chat_tokenizer_state)
# Use model and tokenizer from state to get text response
@gpu_decorator
def generate_text_response(conv_state, system_prompt):
"""Generate text response from AI"""
system_prompt_state = [{"role": "system", "content": system_prompt}]
response = chat_model_inference(system_prompt_state + conv_state, chat_model_state, chat_tokenizer_state)
conv_state.append({"role": "assistant", "content": response})
history[-1] = (text, response)
return history, conv_state, "", None
return conv_state
@gpu_decorator
def generate_audio_response(
history, ref_audio, ref_text, ref_text_file, remove_silence, randomize_seed, seed_input
):
def generate_audio_response(history, ref_audio, ref_text, remove_silence, randomize_seed, seed_input):
"""Generate TTS audio for AI response"""
if not history or not ref_audio:
return None, ref_text, seed_input
last_user_message, last_ai_response = history[-1]
if not last_ai_response:
last_ai_response = history[-1]["content"]
if not last_ai_response or history[-1]["role"] != "assistant":
return None, ref_text, seed_input
# Determine the seed to use
if randomize_seed:
seed = np.random.randint(0, 2**31)
seed = np.random.randint(0, 2**31 - 1)
else:
try:
seed = int(seed_input)
if seed < 0:
gr.Warning("Seed must be a non-negative integer. Using random seed instead.")
seed = np.random.randint(0, 2**31)
except ValueError:
gr.Warning("Invalid seed value. Using random seed instead.")
seed = np.random.randint(0, 2**31)
# Use text from file if provided, otherwise use direct text input
ref_text = read_text_file(ref_text_file) or ref_text
seed = seed_input
if seed < 0 or seed > 2**31 - 1:
gr.Warning("Seed must in range 0 ~ 2147483647. Using random seed instead.")
seed = np.random.randint(0, 2**31 - 1)
audio_result, _, ref_text_out = infer(
ref_audio,
ref_text,
None,
last_ai_response,
None,
tts_model_choice,
remove_silence,
seed=seed,
@@ -906,113 +808,50 @@ Have a conversation with an AI using your reference voice!
speed=1.0,
show_info=print, # show_info=print no pull to top when generating
)
return audio_result, ref_text_out, str(seed)
return audio_result, ref_text_out, seed
def clear_conversation():
"""Reset the conversation"""
return [], [
{
"role": "system",
"content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
}
]
return [], None
def update_system_prompt(new_prompt):
"""Update the system prompt and reset the conversation"""
new_conv_state = [{"role": "system", "content": new_prompt}]
return [], new_conv_state
@gpu_decorator
def update_text_from_file(file):
"""Update the text input when a .txt file is uploaded"""
text = read_text_file(file)
return gr.update(value=text), None
ref_text_file_chat.change(
update_ref_text_from_file,
ref_text_file_chat.upload(
load_text_from_file,
inputs=[ref_text_file_chat],
outputs=[ref_text_chat],
)
text_file_chat.change(
update_text_from_file,
inputs=[text_file_chat],
outputs=[text_input_chat, text_file_chat],
)
for user_operation in [audio_input_chat.stop_recording, text_input_chat.submit, send_btn_chat.click]:
user_operation(
process_audio_input,
inputs=[chatbot_interface, audio_input_chat, text_input_chat],
outputs=[chatbot_interface],
).then(
generate_text_response,
inputs=[chatbot_interface, system_prompt_chat],
outputs=[chatbot_interface],
).then(
generate_audio_response,
inputs=[
chatbot_interface,
ref_audio_chat,
ref_text_chat,
remove_silence_chat,
randomize_seed_chat,
seed_input_chat,
],
outputs=[audio_output_chat, ref_text_chat, seed_input_chat],
).then(
lambda: [None, None],
None,
[audio_input_chat, text_input_chat],
)
# Handle audio input
audio_input_chat.stop_recording(
process_audio_input,
inputs=[audio_input_chat, text_input_chat, text_file_chat, chatbot_interface, conversation_state],
outputs=[chatbot_interface, conversation_state, text_input_chat, text_file_chat],
).then(
generate_audio_response,
inputs=[
chatbot_interface,
ref_audio_chat,
ref_text_chat,
ref_text_file_chat,
remove_silence_chat,
randomize_seed_chat,
seed_input_chat,
],
outputs=[audio_output_chat, ref_text_chat, seed_input_chat],
).then(
lambda: None,
None,
audio_input_chat,
)
# Handle text input
text_input_chat.submit(
process_audio_input,
inputs=[audio_input_chat, text_input_chat, text_file_chat, chatbot_interface, conversation_state],
outputs=[chatbot_interface, conversation_state, text_input_chat, text_file_chat],
).then(
generate_audio_response,
inputs=[
chatbot_interface,
ref_audio_chat,
ref_text_chat,
ref_text_file_chat,
remove_silence_chat,
randomize_seed_chat,
seed_input_chat,
],
outputs=[audio_output_chat, ref_text_chat, seed_input_chat],
)
# Handle send button
send_btn_chat.click(
process_audio_input,
inputs=[audio_input_chat, text_input_chat, text_file_chat, chatbot_interface, conversation_state],
outputs=[chatbot_interface, conversation_state, text_input_chat, text_file_chat],
).then(
generate_audio_response,
inputs=[
chatbot_interface,
ref_audio_chat,
ref_text_chat,
ref_text_file_chat,
remove_silence_chat,
randomize_seed_chat,
seed_input_chat,
],
outputs=[audio_output_chat, ref_text_chat, seed_input_chat],
)
# Handle clear button
clear_btn_chat.click(
clear_conversation,
outputs=[chatbot_interface, conversation_state],
)
# Handle system prompt change and reset conversation
system_prompt_chat.change(
update_system_prompt,
inputs=system_prompt_chat,
outputs=[chatbot_interface, conversation_state],
)
# Handle clear button or system prompt change and reset conversation
for user_operation in [clear_btn_chat.click, system_prompt_chat.change, chatbot_interface.clear]:
user_operation(
clear_conversation,
outputs=[chatbot_interface, audio_output_chat],
)
with gr.Blocks() as app:
@@ -1027,9 +866,9 @@ This is {"a local web UI for [F5 TTS](https://github.com/SWivid/F5-TTS)" if not
The checkpoints currently support English and Chinese.
If you're having issues, try converting your заборreference audio to WAV or MP3, clipping it to 12s with ✂ in the bottom right corner (otherwise might have non-optimal auto-trimmed result).
If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 12s with ✂ in the bottom right corner (otherwise might have non-optimal auto-trimmed result).
**NOTE: Reference text will be automatically transcribed with Whisper if not provided via text or .txt file. For best results, keep your reference clips short (<12s). Ensure the audio is fully uploaded before generating.**
**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<12s). Ensure the audio is fully uploaded before generating.**
"""
)