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| # ruff: noqa: E402 | |
| # Above allows ruff to ignore E402: module level import not at top of file | |
| import re | |
| import tempfile | |
| import click | |
| import gradio as gr | |
| import numpy as np | |
| import soundfile as sf | |
| import torchaudio | |
| from cached_path import cached_path | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| try: | |
| import spaces | |
| USING_SPACES = True | |
| except ImportError: | |
| USING_SPACES = False | |
| def gpu_decorator(func): | |
| if USING_SPACES: | |
| return spaces.GPU(func) | |
| else: | |
| return func | |
| from f5_tts.model import DiT, UNetT | |
| from f5_tts.infer.utils_infer import ( | |
| load_vocoder, | |
| load_model, | |
| preprocess_ref_audio_text, | |
| infer_process, | |
| remove_silence_for_generated_wav, | |
| save_spectrogram, | |
| ) | |
| vocos = load_vocoder() | |
| # load models | |
| F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) | |
| F5TTS_ema_model = load_model( | |
| DiT, F5TTS_model_cfg, str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")) | |
| ) | |
| E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) | |
| E2TTS_ema_model = load_model( | |
| UNetT, E2TTS_model_cfg, str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors")) | |
| ) | |
| chat_model_state = None | |
| chat_tokenizer_state = None | |
| def generate_response(messages, model, tokenizer): | |
| """Generate response using Qwen""" | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True, | |
| ) | |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) | |
| generated_ids = model.generate( | |
| **model_inputs, | |
| max_new_tokens=512, | |
| temperature=0.7, | |
| top_p=0.95, | |
| ) | |
| generated_ids = [ | |
| output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) | |
| ] | |
| return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15, speed=1): | |
| ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=gr.Info) | |
| if model == "F5-TTS": | |
| ema_model = F5TTS_ema_model | |
| elif model == "E2-TTS": | |
| ema_model = E2TTS_ema_model | |
| final_wave, final_sample_rate, combined_spectrogram = infer_process( | |
| ref_audio, | |
| ref_text, | |
| gen_text, | |
| ema_model, | |
| cross_fade_duration=cross_fade_duration, | |
| speed=speed, | |
| show_info=gr.Info, | |
| progress=gr.Progress(), | |
| ) | |
| # Remove silence | |
| if remove_silence: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: | |
| sf.write(f.name, final_wave, final_sample_rate) | |
| remove_silence_for_generated_wav(f.name) | |
| final_wave, _ = torchaudio.load(f.name) | |
| final_wave = final_wave.squeeze().cpu().numpy() | |
| # Save the spectrogram | |
| with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: | |
| spectrogram_path = tmp_spectrogram.name | |
| save_spectrogram(combined_spectrogram, spectrogram_path) | |
| return (final_sample_rate, final_wave), spectrogram_path | |
| 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 initial chunk generation and podcast app exploration | |
| * [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation & voice chat | |
| """) | |
| 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=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") | |
| 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, | |
| 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.", | |
| ) | |
| 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, | |
| speed_slider, | |
| ], | |
| outputs=[audio_output, spectrogram_output], | |
| ) | |
| def parse_speechtypes_text(gen_text): | |
| # Pattern to find {speechtype} | |
| 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_multistyle: | |
| # New section for emotional generation | |
| gr.Markdown( | |
| """ | |
| # Multiple Speech-Type Generation | |
| This section allows you to generate multiple speech types or multiple people's voices. Enter your text in the format shown below, and the system will generate speech using the appropriate type. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified. | |
| """ | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| **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! | |
| {Whisper} I'll just go back home and cry now. | |
| {Shouting} Why me?! | |
| """ | |
| ) | |
| gr.Markdown( | |
| """ | |
| **Example Input 2:** | |
| {Speaker1_Happy} Hello, I'd like to order a sandwich please. | |
| {Speaker2_Regular} Sorry, we're out of bread. | |
| {Speaker1_Sad} I really wanted a sandwich though... | |
| {Speaker2_Whisper} I'll give you the last one I was hiding. | |
| """ | |
| ) | |
| gr.Markdown( | |
| "Upload different audio clips for each speech type. The first speech type 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") | |
| 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 99 more) | |
| max_speech_types = 100 | |
| speech_type_rows = [] | |
| 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(visible=False) as row: | |
| with gr.Column(): | |
| name_input = gr.Textbox(label="Speech Type Name") | |
| delete_btn = gr.Button("Delete", variant="secondary") | |
| audio_input = gr.Audio(label="Reference Audio", type="filepath") | |
| ref_text_input = gr.Textbox(label="Reference Text", lines=2) | |
| speech_type_rows.append(row) | |
| 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 rows | |
| row_updates = [] | |
| for i in range(max_speech_types - 1): | |
| if i < speech_type_count: | |
| row_updates.append(gr.update(visible=True)) | |
| else: | |
| row_updates.append(gr.update()) | |
| else: | |
| # Optionally, show a warning | |
| row_updates = [gr.update() for _ in range(max_speech_types - 1)] | |
| return [speech_type_count] + row_updates | |
| add_speech_type_btn.click( | |
| add_speech_type_fn, inputs=speech_type_count, outputs=[speech_type_count] + speech_type_rows | |
| ) | |
| # Function to delete a speech type | |
| def make_delete_speech_type_fn(index): | |
| def delete_speech_type_fn(speech_type_count): | |
| # Prepare updates | |
| row_updates = [] | |
| for i in range(max_speech_types - 1): | |
| if i == index: | |
| row_updates.append(gr.update(visible=False)) | |
| else: | |
| row_updates.append(gr.update()) | |
| speech_type_count = max(0, speech_type_count - 1) | |
| return [speech_type_count] + row_updates | |
| return delete_speech_type_fn | |
| # Update delete button clicks | |
| 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_rows) | |
| # Text input for the prompt | |
| gen_text_input_emotional = 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?!", | |
| ) | |
| # Model choice | |
| model_choice_emotional = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") | |
| with gr.Accordion("Advanced Settings", open=False): | |
| remove_silence_emotional = gr.Checkbox( | |
| label="Remove Silences", | |
| value=False, | |
| ) | |
| # Generate button | |
| generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary") | |
| # Output audio | |
| audio_output_emotional = gr.Audio(label="Synthesized Audio") | |
| 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, 0) | |
| sr, audio_data = audio | |
| generated_audio_segments.append(audio_data) | |
| # Concatenate all audio segments | |
| if generated_audio_segments: | |
| final_audio_data = np.concatenate(generated_audio_segments) | |
| 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_speechtypes_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_chat: | |
| gr.Markdown( | |
| """ | |
| # Voice Chat | |
| Have a conversation with an AI using your reference voice! | |
| 1. Upload a reference audio clip and optionally its transcript. | |
| 2. Load the chat model. | |
| 3. Record your message through your microphone. | |
| 4. The AI will respond using the reference voice. | |
| """ | |
| ) | |
| load_chat_model_btn = gr.Button("Load Chat Model", variant="primary") | |
| chat_interface_container = gr.Column(visible=False) | |
| def load_chat_model(): | |
| global chat_model_state, chat_tokenizer_state | |
| if chat_model_state is None: | |
| show_info = gr.Info | |
| show_info("Loading chat model...") | |
| model_name = "Qwen/Qwen2.5-3B-Instruct" | |
| chat_model_state = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") | |
| chat_tokenizer_state = AutoTokenizer.from_pretrained(model_name) | |
| show_info("Chat model loaded.") | |
| return gr.update(visible=False), gr.update(visible=True) | |
| load_chat_model_btn.click(load_chat_model, outputs=[load_chat_model_btn, chat_interface_container]) | |
| with chat_interface_container: | |
| with gr.Row(): | |
| with gr.Column(): | |
| ref_audio_chat = gr.Audio(label="Reference Audio", type="filepath") | |
| with gr.Column(): | |
| with gr.Accordion("Advanced Settings", open=False): | |
| model_choice_chat = gr.Radio( | |
| choices=["F5-TTS", "E2-TTS"], | |
| label="TTS Model", | |
| value="F5-TTS", | |
| ) | |
| remove_silence_chat = gr.Checkbox( | |
| label="Remove Silences", | |
| value=True, | |
| ) | |
| ref_text_chat = gr.Textbox( | |
| label="Reference Text", | |
| info="Optional: Leave blank to auto-transcribe", | |
| lines=2, | |
| ) | |
| 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, | |
| ) | |
| chatbot_interface = gr.Chatbot(label="Conversation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_output_chat = gr.Audio(autoplay=True) | |
| with gr.Column(): | |
| audio_input_chat = gr.Microphone( | |
| label="Speak your message", | |
| type="filepath", | |
| ) | |
| 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 | |
| def process_audio_input(audio_path, history, conv_state): | |
| """Handle audio input from user""" | |
| if not audio_path: | |
| return history, conv_state, "" | |
| text = "" | |
| text = preprocess_ref_audio_text(audio_path, text)[1] | |
| if not text.strip(): | |
| return history, conv_state, "" | |
| conv_state.append({"role": "user", "content": text}) | |
| history.append((text, None)) | |
| response = generate_response(conv_state, chat_model_state, chat_tokenizer_state) | |
| conv_state.append({"role": "assistant", "content": response}) | |
| history[-1] = (text, response) | |
| return history, conv_state, "" | |
| def generate_audio_response(history, ref_audio, ref_text, model, remove_silence): | |
| """Generate TTS audio for AI response""" | |
| if not history or not ref_audio: | |
| return None | |
| last_user_message, last_ai_response = history[-1] | |
| if not last_ai_response: | |
| return None | |
| audio_result, _ = infer( | |
| ref_audio, | |
| ref_text, | |
| last_ai_response, | |
| model, | |
| remove_silence, | |
| cross_fade_duration=0.15, | |
| speed=1.0, | |
| ) | |
| return audio_result | |
| 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.", | |
| } | |
| ] | |
| 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 | |
| # Handle audio input | |
| audio_input_chat.stop_recording( | |
| process_audio_input, | |
| inputs=[audio_input_chat, chatbot_interface, conversation_state], | |
| outputs=[chatbot_interface, conversation_state], | |
| ).then( | |
| generate_audio_response, | |
| inputs=[chatbot_interface, ref_audio_chat, ref_text_chat, model_choice_chat, remove_silence_chat], | |
| outputs=audio_output_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], | |
| ) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| """ | |
| # E2/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 checkpoints support English and Chinese. | |
| If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, 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.TabbedInterface( | |
| [app_tts, app_multistyle, app_chat, app_credits], | |
| ["TTS", "Multi-Speech", "Voice-Chat", "Credits"], | |
| ) | |
| 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__": | |
| if not USING_SPACES: | |
| main() | |
| else: | |
| app.queue().launch() | |