From 931870e52797a06d620c331919b3ee523e782e79 Mon Sep 17 00:00:00 2001 From: VSlobolinskyi Date: Fri, 21 Mar 2025 20:43:16 +0200 Subject: [PATCH] Revert "Stream functionality preparation" This reverts commit 284264fdbef0ef2bbcfe8b988a42f0b77a55bce1. --- infer-web.py | 4 +- modules/merged_ui/main.py | 13 +- modules/merged_ui/streaming_utils.py | 111 ----- modules/merged_ui/utils.py | 114 ++--- modules/spark_ui/main.py | 66 +++ poetry.lock | 633 +++++++++++++-------------- pyproject.toml | 4 +- 7 files changed, 412 insertions(+), 533 deletions(-) delete mode 100644 modules/merged_ui/streaming_utils.py diff --git a/infer-web.py b/infer-web.py index 81ba9af..eca61fc 100644 --- a/infer-web.py +++ b/infer-web.py @@ -20,9 +20,9 @@ def build_standalone_ui(): if __name__ == "__main__": app = build_merged_ui() if config.iscolab: - app.queue(concurrency_count=511, max_size=1022).launch(share=True) + app.launch(share=True) else: - app.queue(concurrency_count=511, max_size=1022).launch( + app.launch( server_name="localhost", inbrowser=not config.noautoopen, server_port=config.listen_port, diff --git a/modules/merged_ui/main.py b/modules/merged_ui/main.py index 6272479..ade00a7 100644 --- a/modules/merged_ui/main.py +++ b/modules/merged_ui/main.py @@ -1,8 +1,7 @@ import gradio as gr # Import modules from your packages -from merged_ui.utils import modified_get_vc -from merged_ui.streaming_utils import generate_and_process_with_rvc_streaming +from merged_ui.utils import generate_and_process_with_rvc, modified_get_vc from rvc_ui.initialization import config from rvc_ui.main import names, index_paths @@ -18,7 +17,7 @@ def build_merged_ui(): with gr.Tabs(): with gr.TabItem("TTS-to-RVC Pipeline"): gr.Markdown("### Generate speech with Spark TTS and convert with RVC") - gr.Markdown("*Note: Sentences will begin playing as soon as the first one is ready*") + gr.Markdown("*Note: For multi-sentence text, each sentence will be processed separately and then combined.*") # TTS Generation Section with gr.Row(): @@ -131,14 +130,12 @@ def build_merged_ui(): generate_with_rvc_button = gr.Button("Generate with RVC", variant="primary") with gr.Row(): - # Status for updating information (will be updated during processing) vc_output1 = gr.Textbox(label="Output information", lines=10) - # Streaming audio output to support incremental updates - vc_output2 = gr.Audio(label="Audio (updates as sentences are processed)") + vc_output2 = gr.Audio(label="Final concatenated audio") - # Connect generate function to button with streaming output + # Connect generate function to button generate_with_rvc_button.click( - generate_and_process_with_rvc_streaming, + generate_and_process_with_rvc, inputs=[ tts_text_input, prompt_text_input, diff --git a/modules/merged_ui/streaming_utils.py b/modules/merged_ui/streaming_utils.py deleted file mode 100644 index 76deb78..0000000 --- a/modules/merged_ui/streaming_utils.py +++ /dev/null @@ -1,111 +0,0 @@ -import os -import time - -# Import and reuse existing functions -from merged_ui.utils import ( - split_into_sentences, - process_single_sentence, - concatenate_audio_files, - split_into_sentences, - process_single_sentence, - initialize_model -) - -# Initialize the Spark TTS model (moved outside function to avoid reinitializing) -model_dir = "spark/pretrained_models/Spark-TTS-0.5B" -device = 0 -spark_model = initialize_model(model_dir, device=device) - -def generate_and_process_with_rvc_streaming( - text, prompt_text, prompt_wav_upload, prompt_wav_record, - spk_item, vc_transform, f0method, - file_index1, file_index2, index_rate, filter_radius, - resample_sr, rms_mix_rate, protect -): - """ - Stream process TTS and RVC, yielding audio updates as sentences are processed - This is a generator function that yields (status_text, audio_path) tuples - """ - # Ensure TEMP directories exist - os.makedirs("./TEMP/spark", exist_ok=True) - os.makedirs("./TEMP/rvc", exist_ok=True) - os.makedirs("./TEMP/stream", exist_ok=True) - - # Split text into sentences - sentences = split_into_sentences(text) - if not sentences: - yield "No valid text to process.", None - return - - # Get timestamp to create unique session ID for this run - session_id = str(int(time.time())) - - # Process reference speech - prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record - prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text - - # Initialize status - status_messages = [f"Starting to process {len(sentences)} sentences..."] - # Create a list to track processed fragments - processed_fragments = [] - - # Create a temporary directory for streaming - stream_dir = f"./TEMP/stream/{session_id}" - os.makedirs(stream_dir, exist_ok=True) - - # Yield initial status - yield "\n".join(status_messages), None - - # Process each sentence and update the stream - for i, sentence in enumerate(sentences): - # Add current sentence to status - current_msg = f"Processing sentence {i+1}/{len(sentences)}: {sentence[:30]}..." - status_messages.append(current_msg) - yield "\n".join(status_messages), None - - # Process this sentence - spark_path, rvc_path, success, info = process_single_sentence( - i, sentence, prompt_speech, prompt_text_clean, - spk_item, vc_transform, f0method, - file_index1, file_index2, index_rate, filter_radius, - resample_sr, rms_mix_rate, protect, - int(session_id) # Use session ID as base fragment number - ) - - # Update status with processing result - status_messages[-1] = info - - if success and rvc_path and os.path.exists(rvc_path): - processed_fragments.append(rvc_path) - - # Create a streaming update file by concatenating all fragments processed so far - stream_path = os.path.join(stream_dir, f"stream_update_{i+1}.wav") - - # Concatenate all fragments processed so far - concatenate_success = concatenate_audio_files(processed_fragments, stream_path) - - if concatenate_success: - # Yield the updated status and the current stream path - yield "\n".join(status_messages), stream_path - else: - # If concatenation failed, just yield the most recent fragment - yield "\n".join(status_messages), rvc_path - else: - # If processing failed, update status but don't update audio - yield "\n".join(status_messages), None if not processed_fragments else processed_fragments[-1] - - # Final streaming update with completion message - if processed_fragments: - # Create final output file - final_output_path = f"./TEMP/stream/{session_id}/final_output.wav" - concatenate_success = concatenate_audio_files(processed_fragments, final_output_path) - - if concatenate_success: - status_messages.append(f"\nAll {len(sentences)} sentences processed successfully!") - yield "\n".join(status_messages), final_output_path - else: - status_messages.append("\nWarning: Failed to create final concatenated file.") - yield "\n".join(status_messages), processed_fragments[-1] - else: - status_messages.append("\nNo sentences were successfully processed.") - yield "\n".join(status_messages), None \ No newline at end of file diff --git a/modules/merged_ui/utils.py b/modules/merged_ui/utils.py index eacda36..7e95ee4 100644 --- a/modules/merged_ui/utils.py +++ b/modules/merged_ui/utils.py @@ -1,87 +1,20 @@ -from datetime import datetime -import logging import os -import platform import shutil import re import numpy as np +from time import sleep import soundfile as sf from pydub import AudioSegment -import torch # Import modules from your packages -from spark.cli.SparkTTS import SparkTTS from rvc_ui.initialization import vc +from spark_ui.main import initialize_model, run_tts +from spark.sparktts.utils.token_parser import LEVELS_MAP_UI # Initialize the Spark TTS model (moved outside function to avoid reinitializing) model_dir = "spark/pretrained_models/Spark-TTS-0.5B" device = 0 - -def initialize_model(model_dir, device): - """Load the model once at the beginning.""" - logging.info(f"Loading model from: {model_dir}") - - # Determine appropriate device based on platform and availability - if platform.system() == "Darwin": - # macOS with MPS support (Apple Silicon) - device = torch.device(f"mps:{device}") - logging.info(f"Using MPS device: {device}") - elif torch.cuda.is_available(): - # System with CUDA support - device = torch.device(f"cuda:{device}") - logging.info(f"Using CUDA device: {device}") - else: - # Fall back to CPU - device = torch.device("cpu") - logging.info("GPU acceleration not available, using CPU") - - model = SparkTTS(model_dir, device) - return model - - -def run_tts( - text, - prompt_text=None, - prompt_speech=None, - gender=None, - pitch=None, - speed=None, - save_dir="TEMP/spark", # Updated default save directory - save_filename=None, # New parameter to specify filename -): - """Perform TTS inference and save the generated audio.""" - model = initialize_model(model_dir, device=device) - logging.info(f"Saving audio to: {save_dir}") - - if prompt_text is not None: - prompt_text = None if len(prompt_text) <= 1 else prompt_text - - # Ensure the save directory exists - os.makedirs(save_dir, exist_ok=True) - - # Determine the save path based on save_filename if provided; otherwise, use a timestamp - if save_filename: - save_path = os.path.join(save_dir, save_filename) - else: - timestamp = datetime.now().strftime("%Y%m%d%H%M%S") - save_path = os.path.join(save_dir, f"{timestamp}.wav") - - logging.info("Starting inference...") - - # Perform inference and save the output audio - with torch.no_grad(): - wav = model.inference( - text, - prompt_speech, - prompt_text, - gender, - pitch, - speed, - ) - sf.write(save_path, wav, samplerate=16000) - - logging.info(f"Audio saved at: {save_path}") - return save_path +spark_model = initialize_model(model_dir, device=device) def split_into_sentences(text): """ @@ -107,26 +40,34 @@ def process_single_sentence( base_fragment_num ): """ - Process a single sentence through the TTS and RVC pipeline. + Process a single sentence through the TTS and RVC pipeline + + Args: + sentence_index (int): Index of the sentence in the original text + sentence (str): The sentence text to process + ... (other parameters are the same as generate_and_process_with_rvc) + + Returns: + tuple: (spark_output_path, rvc_output_path, success, info_message) """ fragment_num = base_fragment_num + sentence_index - - # Generate TTS audio for this sentence, saving directly to the correct location + + # Generate TTS audio for this sentence tts_path = run_tts( sentence, + spark_model, prompt_text=prompt_text_clean, - prompt_speech=prompt_speech, - save_dir="./TEMP/spark", - save_filename=f"fragment_{fragment_num}.wav" + prompt_speech=prompt_speech ) - + # Make sure we have a TTS file to process if not tts_path or not os.path.exists(tts_path): return None, None, False, f"Failed to generate TTS audio for sentence: {sentence}" - - # Use the tts_path as the Spark output (no need to copy) - spark_output_path = tts_path - + + # Save Spark output to TEMP/spark + spark_output_path = f"./TEMP/spark/fragment_{fragment_num}.wav" + shutil.copy2(tts_path, spark_output_path) + # Call RVC processing function f0_file = None # We're not using an F0 curve file in this pipeline output_info, output_audio = vc.vc_single( @@ -134,11 +75,12 @@ def process_single_sentence( file_index1, file_index2, index_rate, filter_radius, resample_sr, rms_mix_rate, protect ) - + # Save RVC output to TEMP/rvc directory rvc_output_path = f"./TEMP/rvc/fragment_{fragment_num}.wav" rvc_saved = False - + + # Try different ways to save the RVC output based on common formats try: if isinstance(output_audio, str) and os.path.exists(output_audio): # Case 1: output_audio is a file path string @@ -157,7 +99,7 @@ def process_single_sentence( rvc_saved = True except Exception as e: output_info += f"\nError saving RVC output: {str(e)}" - + # Prepare info message info_message = f"Sentence {sentence_index+1}: {sentence[:30]}{'...' if len(sentence) > 30 else ''}\n" info_message += f" - Spark output: {spark_output_path}\n" @@ -165,7 +107,7 @@ def process_single_sentence( info_message += f" - RVC output: {rvc_output_path}" else: info_message += f" - Could not save RVC output to {rvc_output_path}" - + return spark_output_path, rvc_output_path, rvc_saved, info_message def concatenate_audio_files(file_paths, output_path, sample_rate=44100): diff --git a/modules/spark_ui/main.py b/modules/spark_ui/main.py index b949011..faa9529 100644 --- a/modules/spark_ui/main.py +++ b/modules/spark_ui/main.py @@ -11,6 +11,72 @@ from datetime import datetime from spark.cli.SparkTTS import SparkTTS from spark.sparktts.utils.token_parser import LEVELS_MAP_UI + +def initialize_model(model_dir, device): + """Load the model once at the beginning.""" + logging.info(f"Loading model from: {model_dir}") + + # Determine appropriate device based on platform and availability + if platform.system() == "Darwin": + # macOS with MPS support (Apple Silicon) + device = torch.device(f"mps:{device}") + logging.info(f"Using MPS device: {device}") + elif torch.cuda.is_available(): + # System with CUDA support + device = torch.device(f"cuda:{device}") + logging.info(f"Using CUDA device: {device}") + else: + # Fall back to CPU + device = torch.device("cpu") + logging.info("GPU acceleration not available, using CPU") + + model = SparkTTS(model_dir, device) + return model + + +def run_tts( + text, + model, + prompt_text=None, + prompt_speech=None, + gender=None, + pitch=None, + speed=None, + save_dir="spark/example/results", +): + """Perform TTS inference and save the generated audio.""" + logging.info(f"Saving audio to: {save_dir}") + + if prompt_text is not None: + prompt_text = None if len(prompt_text) <= 1 else prompt_text + + # Ensure the save directory exists + os.makedirs(save_dir, exist_ok=True) + + # Generate unique filename using timestamp + timestamp = datetime.now().strftime("%Y%m%d%H%M%S") + save_path = os.path.join(save_dir, f"{timestamp}.wav") + + logging.info("Starting inference...") + + # Perform inference and save the output audio + with torch.no_grad(): + wav = model.inference( + text, + prompt_speech, + prompt_text, + gender, + pitch, + speed, + ) + + sf.write(save_path, wav, samplerate=16000) + + logging.info(f"Audio saved at: {save_path}") + + return save_path + + def build_spark_ui(): model_dir = "spark/pretrained_models/Spark-TTS-0.5B" device = 0 diff --git a/poetry.lock b/poetry.lock index 702630d..5212811 100644 --- a/poetry.lock +++ b/poetry.lock @@ -25,30 +25,17 @@ files = [ ] [[package]] -name = "altair" -version = "5.5.0" -description = "Vega-Altair: A declarative statistical visualization library for Python." +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" optional = false -python-versions = ">=3.9" +python-versions = ">=3.8" groups = ["main"] files = [ - 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