mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-05 04:08:58 +08:00
Revert "Stream functionality preparation"
This reverts commit 284264fdbef0ef2bbcfe8b988a42f0b77a55bce1.
This commit is contained in:
VSlobolinskyi
parent
284264fdbe
commit
931870e527
@ -20,9 +20,9 @@ def build_standalone_ui():
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if __name__ == "__main__":
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app = build_merged_ui()
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if config.iscolab:
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app.queue(concurrency_count=511, max_size=1022).launch(share=True)
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app.launch(share=True)
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else:
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app.queue(concurrency_count=511, max_size=1022).launch(
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app.launch(
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server_name="localhost",
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inbrowser=not config.noautoopen,
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server_port=config.listen_port,
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@ -1,8 +1,7 @@
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import gradio as gr
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# Import modules from your packages
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from merged_ui.utils import modified_get_vc
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from merged_ui.streaming_utils import generate_and_process_with_rvc_streaming
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from merged_ui.utils import generate_and_process_with_rvc, modified_get_vc
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from rvc_ui.initialization import config
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from rvc_ui.main import names, index_paths
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@ -18,7 +17,7 @@ def build_merged_ui():
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with gr.Tabs():
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with gr.TabItem("TTS-to-RVC Pipeline"):
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gr.Markdown("### Generate speech with Spark TTS and convert with RVC")
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gr.Markdown("*Note: Sentences will begin playing as soon as the first one is ready*")
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gr.Markdown("*Note: For multi-sentence text, each sentence will be processed separately and then combined.*")
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# TTS Generation Section
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with gr.Row():
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@ -131,14 +130,12 @@ def build_merged_ui():
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generate_with_rvc_button = gr.Button("Generate with RVC", variant="primary")
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with gr.Row():
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# Status for updating information (will be updated during processing)
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vc_output1 = gr.Textbox(label="Output information", lines=10)
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# Streaming audio output to support incremental updates
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vc_output2 = gr.Audio(label="Audio (updates as sentences are processed)")
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vc_output2 = gr.Audio(label="Final concatenated audio")
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# Connect generate function to button with streaming output
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# Connect generate function to button
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generate_with_rvc_button.click(
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generate_and_process_with_rvc_streaming,
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generate_and_process_with_rvc,
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inputs=[
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tts_text_input,
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prompt_text_input,
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@ -1,111 +0,0 @@
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import os
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import time
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# Import and reuse existing functions
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from merged_ui.utils import (
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split_into_sentences,
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process_single_sentence,
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concatenate_audio_files,
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split_into_sentences,
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process_single_sentence,
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initialize_model
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)
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# Initialize the Spark TTS model (moved outside function to avoid reinitializing)
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model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
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device = 0
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spark_model = initialize_model(model_dir, device=device)
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def generate_and_process_with_rvc_streaming(
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text, prompt_text, prompt_wav_upload, prompt_wav_record,
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spk_item, vc_transform, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect
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):
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"""
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Stream process TTS and RVC, yielding audio updates as sentences are processed
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This is a generator function that yields (status_text, audio_path) tuples
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"""
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# Ensure TEMP directories exist
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os.makedirs("./TEMP/spark", exist_ok=True)
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os.makedirs("./TEMP/rvc", exist_ok=True)
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os.makedirs("./TEMP/stream", exist_ok=True)
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# Split text into sentences
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sentences = split_into_sentences(text)
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if not sentences:
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yield "No valid text to process.", None
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return
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# Get timestamp to create unique session ID for this run
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session_id = str(int(time.time()))
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# Process reference speech
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prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
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prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text
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# Initialize status
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status_messages = [f"Starting to process {len(sentences)} sentences..."]
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# Create a list to track processed fragments
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processed_fragments = []
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# Create a temporary directory for streaming
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stream_dir = f"./TEMP/stream/{session_id}"
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os.makedirs(stream_dir, exist_ok=True)
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# Yield initial status
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yield "\n".join(status_messages), None
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# Process each sentence and update the stream
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for i, sentence in enumerate(sentences):
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# Add current sentence to status
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current_msg = f"Processing sentence {i+1}/{len(sentences)}: {sentence[:30]}..."
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status_messages.append(current_msg)
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yield "\n".join(status_messages), None
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# Process this sentence
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spark_path, rvc_path, success, info = process_single_sentence(
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i, sentence, prompt_speech, prompt_text_clean,
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spk_item, vc_transform, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect,
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int(session_id) # Use session ID as base fragment number
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)
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# Update status with processing result
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status_messages[-1] = info
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if success and rvc_path and os.path.exists(rvc_path):
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processed_fragments.append(rvc_path)
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# Create a streaming update file by concatenating all fragments processed so far
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stream_path = os.path.join(stream_dir, f"stream_update_{i+1}.wav")
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# Concatenate all fragments processed so far
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concatenate_success = concatenate_audio_files(processed_fragments, stream_path)
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if concatenate_success:
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# Yield the updated status and the current stream path
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yield "\n".join(status_messages), stream_path
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else:
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# If concatenation failed, just yield the most recent fragment
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yield "\n".join(status_messages), rvc_path
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else:
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# If processing failed, update status but don't update audio
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yield "\n".join(status_messages), None if not processed_fragments else processed_fragments[-1]
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# Final streaming update with completion message
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if processed_fragments:
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# Create final output file
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final_output_path = f"./TEMP/stream/{session_id}/final_output.wav"
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concatenate_success = concatenate_audio_files(processed_fragments, final_output_path)
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if concatenate_success:
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status_messages.append(f"\nAll {len(sentences)} sentences processed successfully!")
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yield "\n".join(status_messages), final_output_path
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else:
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status_messages.append("\nWarning: Failed to create final concatenated file.")
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yield "\n".join(status_messages), processed_fragments[-1]
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else:
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status_messages.append("\nNo sentences were successfully processed.")
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yield "\n".join(status_messages), None
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@ -1,87 +1,20 @@
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from datetime import datetime
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import logging
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import os
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import platform
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import shutil
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import re
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import numpy as np
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from time import sleep
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import soundfile as sf
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from pydub import AudioSegment
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import torch
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# Import modules from your packages
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from spark.cli.SparkTTS import SparkTTS
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from rvc_ui.initialization import vc
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from spark_ui.main import initialize_model, run_tts
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from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
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# Initialize the Spark TTS model (moved outside function to avoid reinitializing)
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model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
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device = 0
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def initialize_model(model_dir, device):
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"""Load the model once at the beginning."""
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logging.info(f"Loading model from: {model_dir}")
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# Determine appropriate device based on platform and availability
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if platform.system() == "Darwin":
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# macOS with MPS support (Apple Silicon)
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device = torch.device(f"mps:{device}")
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logging.info(f"Using MPS device: {device}")
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elif torch.cuda.is_available():
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# System with CUDA support
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device = torch.device(f"cuda:{device}")
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logging.info(f"Using CUDA device: {device}")
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else:
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# Fall back to CPU
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device = torch.device("cpu")
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logging.info("GPU acceleration not available, using CPU")
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model = SparkTTS(model_dir, device)
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return model
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def run_tts(
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text,
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prompt_text=None,
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prompt_speech=None,
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gender=None,
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pitch=None,
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speed=None,
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save_dir="TEMP/spark", # Updated default save directory
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save_filename=None, # New parameter to specify filename
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):
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"""Perform TTS inference and save the generated audio."""
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model = initialize_model(model_dir, device=device)
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logging.info(f"Saving audio to: {save_dir}")
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if prompt_text is not None:
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prompt_text = None if len(prompt_text) <= 1 else prompt_text
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# Ensure the save directory exists
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os.makedirs(save_dir, exist_ok=True)
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# Determine the save path based on save_filename if provided; otherwise, use a timestamp
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if save_filename:
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save_path = os.path.join(save_dir, save_filename)
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else:
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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save_path = os.path.join(save_dir, f"{timestamp}.wav")
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logging.info("Starting inference...")
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# Perform inference and save the output audio
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with torch.no_grad():
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wav = model.inference(
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text,
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prompt_speech,
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prompt_text,
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gender,
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pitch,
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speed,
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)
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sf.write(save_path, wav, samplerate=16000)
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logging.info(f"Audio saved at: {save_path}")
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return save_path
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spark_model = initialize_model(model_dir, device=device)
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def split_into_sentences(text):
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"""
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@ -107,25 +40,33 @@ def process_single_sentence(
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base_fragment_num
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):
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"""
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Process a single sentence through the TTS and RVC pipeline.
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Process a single sentence through the TTS and RVC pipeline
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Args:
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sentence_index (int): Index of the sentence in the original text
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sentence (str): The sentence text to process
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... (other parameters are the same as generate_and_process_with_rvc)
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Returns:
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tuple: (spark_output_path, rvc_output_path, success, info_message)
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"""
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fragment_num = base_fragment_num + sentence_index
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# Generate TTS audio for this sentence, saving directly to the correct location
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# Generate TTS audio for this sentence
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tts_path = run_tts(
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sentence,
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spark_model,
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prompt_text=prompt_text_clean,
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prompt_speech=prompt_speech,
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save_dir="./TEMP/spark",
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save_filename=f"fragment_{fragment_num}.wav"
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prompt_speech=prompt_speech
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)
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# Make sure we have a TTS file to process
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if not tts_path or not os.path.exists(tts_path):
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return None, None, False, f"Failed to generate TTS audio for sentence: {sentence}"
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# Use the tts_path as the Spark output (no need to copy)
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spark_output_path = tts_path
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# Save Spark output to TEMP/spark
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spark_output_path = f"./TEMP/spark/fragment_{fragment_num}.wav"
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shutil.copy2(tts_path, spark_output_path)
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# Call RVC processing function
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f0_file = None # We're not using an F0 curve file in this pipeline
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@ -139,6 +80,7 @@ def process_single_sentence(
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rvc_output_path = f"./TEMP/rvc/fragment_{fragment_num}.wav"
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rvc_saved = False
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# Try different ways to save the RVC output based on common formats
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try:
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if isinstance(output_audio, str) and os.path.exists(output_audio):
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# Case 1: output_audio is a file path string
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@ -11,6 +11,72 @@ from datetime import datetime
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from spark.cli.SparkTTS import SparkTTS
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from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
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def initialize_model(model_dir, device):
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"""Load the model once at the beginning."""
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logging.info(f"Loading model from: {model_dir}")
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# Determine appropriate device based on platform and availability
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if platform.system() == "Darwin":
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# macOS with MPS support (Apple Silicon)
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device = torch.device(f"mps:{device}")
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logging.info(f"Using MPS device: {device}")
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elif torch.cuda.is_available():
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# System with CUDA support
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device = torch.device(f"cuda:{device}")
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logging.info(f"Using CUDA device: {device}")
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else:
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# Fall back to CPU
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device = torch.device("cpu")
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logging.info("GPU acceleration not available, using CPU")
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model = SparkTTS(model_dir, device)
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return model
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def run_tts(
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text,
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model,
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prompt_text=None,
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prompt_speech=None,
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gender=None,
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pitch=None,
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speed=None,
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save_dir="spark/example/results",
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):
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"""Perform TTS inference and save the generated audio."""
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logging.info(f"Saving audio to: {save_dir}")
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if prompt_text is not None:
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prompt_text = None if len(prompt_text) <= 1 else prompt_text
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# Ensure the save directory exists
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os.makedirs(save_dir, exist_ok=True)
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# Generate unique filename using timestamp
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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save_path = os.path.join(save_dir, f"{timestamp}.wav")
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logging.info("Starting inference...")
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# Perform inference and save the output audio
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with torch.no_grad():
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wav = model.inference(
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text,
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prompt_speech,
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prompt_text,
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gender,
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pitch,
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speed,
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)
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sf.write(save_path, wav, samplerate=16000)
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logging.info(f"Audio saved at: {save_path}")
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return save_path
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def build_spark_ui():
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model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
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device = 0
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633
poetry.lock
generated
633
poetry.lock
generated
@ -25,30 +25,17 @@ files = [
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]
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[[package]]
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name = "altair"
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version = "5.5.0"
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description = "Vega-Altair: A declarative statistical visualization library for Python."
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name = "annotated-types"
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version = "0.7.0"
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description = "Reusable constraint types to use with typing.Annotated"
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optional = false
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python-versions = ">=3.9"
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python-versions = ">=3.8"
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groups = ["main"]
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files = [
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{file = "altair-5.5.0-py3-none-any.whl", hash = "sha256:91a310b926508d560fe0148d02a194f38b824122641ef528113d029fcd129f8c"},
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{file = "altair-5.5.0.tar.gz", hash = "sha256:d960ebe6178c56de3855a68c47b516be38640b73fb3b5111c2a9ca90546dd73d"},
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{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
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{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
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]
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[package.dependencies]
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jinja2 = "*"
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jsonschema = ">=3.0"
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narwhals = ">=1.14.2"
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packaging = "*"
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typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.14\""}
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[package.extras]
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all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=1.1.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.7.0)"]
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dev = ["duckdb (>=1.0)", "geopandas", "hatch (>=1.13.0)", "ipython[kernel]", "mistune", "mypy", "pandas (>=1.1.3)", "pandas-stubs", "polars (>=0.20.3)", "pyarrow-stubs", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
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doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
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save = ["vl-convert-python (>=1.7.0)"]
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[[package]]
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name = "antlr4-python3-runtime"
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version = "4.9.3"
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@ -81,26 +68,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
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test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\""]
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trio = ["trio (>=0.23)"]
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[[package]]
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name = "attrs"
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version = "25.3.0"
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description = "Classes Without Boilerplate"
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optional = false
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python-versions = ">=3.8"
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groups = ["main"]
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files = [
|
||||
{file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
|
||||
{file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
|
||||
cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
|
||||
dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
|
||||
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
|
||||
tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
|
||||
tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
|
||||
|
||||
[[package]]
|
||||
name = "audioread"
|
||||
version = "3.0.1"
|
||||
@ -878,25 +845,24 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.88.0"
|
||||
version = "0.115.11"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "fastapi-0.88.0-py3-none-any.whl", hash = "sha256:263b718bb384422fe3d042ffc9a0c8dece5e034ab6586ff034f6b4b1667c3eee"},
|
||||
{file = "fastapi-0.88.0.tar.gz", hash = "sha256:915bf304180a0e7c5605ec81097b7d4cd8826ff87a02bb198e336fb9f3b5ff02"},
|
||||
{file = "fastapi-0.115.11-py3-none-any.whl", hash = "sha256:32e1541b7b74602e4ef4a0260ecaf3aadf9d4f19590bba3e1bf2ac4666aa2c64"},
|
||||
{file = "fastapi-0.115.11.tar.gz", hash = "sha256:cc81f03f688678b92600a65a5e618b93592c65005db37157147204d8924bf94f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pydantic = ">=1.6.2,<1.7 || >1.7,<1.7.1 || >1.7.1,<1.7.2 || >1.7.2,<1.7.3 || >1.7.3,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
|
||||
starlette = "0.22.0"
|
||||
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
|
||||
starlette = ">=0.40.0,<0.47.0"
|
||||
typing-extensions = ">=4.8.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["email-validator (>=1.1.1)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
|
||||
dev = ["pre-commit (>=2.17.0,<3.0.0)", "ruff (==0.0.138)", "uvicorn[standard] (>=0.12.0,<0.19.0)"]
|
||||
doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-markdownextradata-plugin (>=0.1.7,<0.3.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pyyaml (>=5.3.1,<7.0.0)", "typer[all] (>=0.6.1,<0.7.0)"]
|
||||
test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==22.10.0)", "coverage[toml] (>=6.5.0,<7.0)", "databases[sqlite] (>=0.3.2,<0.7.0)", "email-validator (>=1.1.1,<2.0.0)", "flask (>=1.1.2,<3.0.0)", "httpx (>=0.23.0,<0.24.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.982)", "orjson (>=3.2.1,<4.0.0)", "passlib[bcrypt] (>=1.7.2,<2.0.0)", "peewee (>=3.13.3,<4.0.0)", "pytest (>=7.1.3,<8.0.0)", "python-jose[cryptography] (>=3.3.0,<4.0.0)", "python-multipart (>=0.0.5,<0.0.6)", "pyyaml (>=5.3.1,<7.0.0)", "ruff (==0.0.138)", "sqlalchemy (>=1.3.18,<=1.4.41)", "types-orjson (==3.6.2)", "types-ujson (==5.5.0)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,<6.0.0)"]
|
||||
all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
|
||||
standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "ffmpeg-python"
|
||||
@ -1125,66 +1091,67 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "gradio"
|
||||
version = "3.50.0"
|
||||
version = "4.44.1"
|
||||
description = "Python library for easily interacting with trained machine learning models"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "gradio-3.50.0-py3-none-any.whl", hash = "sha256:391829e050a9fc68f593b175537af12dfbb0529dd746e1a3b1685dcd68c24eab"},
|
||||
{file = "gradio-3.50.0.tar.gz", hash = "sha256:78a8f2b2aec9795513bfcebfeb8e8071eb6c19cec391fe0e470d65b3591c25f2"},
|
||||
{file = "gradio-4.44.1-py3-none-any.whl", hash = "sha256:c908850c638e4a176b22f95a758ce6a63ffbc2a7a5a74b23186ceeeedc23f4d9"},
|
||||
{file = "gradio-4.44.1.tar.gz", hash = "sha256:a68a52498ac6b63f8864ef84bf7866a70e7d07ebe913edf921e1d2a3708ad5ae"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiofiles = ">=22.0,<24.0"
|
||||
altair = ">=4.2.0,<6.0"
|
||||
fastapi = "*"
|
||||
anyio = ">=3.0,<5.0"
|
||||
fastapi = "<1.0"
|
||||
ffmpy = "*"
|
||||
gradio-client = "0.6.1"
|
||||
httpx = "*"
|
||||
huggingface-hub = ">=0.14.0"
|
||||
gradio-client = "1.3.0"
|
||||
httpx = ">=0.24.1"
|
||||
huggingface-hub = ">=0.19.3"
|
||||
importlib-resources = ">=1.3,<7.0"
|
||||
jinja2 = "<4.0"
|
||||
markupsafe = ">=2.0,<3.0"
|
||||
matplotlib = ">=3.0,<4.0"
|
||||
numpy = ">=1.0,<2.0"
|
||||
numpy = ">=1.0,<3.0"
|
||||
orjson = ">=3.0,<4.0"
|
||||
packaging = "*"
|
||||
pandas = ">=1.0,<3.0"
|
||||
pillow = ">=8.0,<11.0"
|
||||
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<3.0.0"
|
||||
pydantic = ">=2.0"
|
||||
pydub = "*"
|
||||
python-multipart = "*"
|
||||
python-multipart = ">=0.0.9"
|
||||
pyyaml = ">=5.0,<7.0"
|
||||
requests = ">=2.0,<3.0"
|
||||
ruff = {version = ">=0.2.2", markers = "sys_platform != \"emscripten\""}
|
||||
semantic-version = ">=2.0,<3.0"
|
||||
tomlkit = "0.12.0"
|
||||
typer = {version = ">=0.12,<1.0", markers = "sys_platform != \"emscripten\""}
|
||||
typing-extensions = ">=4.0,<5.0"
|
||||
uvicorn = ">=0.14.0"
|
||||
websockets = ">=10.0,<12.0"
|
||||
urllib3 = ">=2.0,<3.0"
|
||||
uvicorn = {version = ">=0.14.0", markers = "sys_platform != \"emscripten\""}
|
||||
|
||||
[package.extras]
|
||||
oauth = ["authlib", "itsdangerous"]
|
||||
|
||||
[[package]]
|
||||
name = "gradio-client"
|
||||
version = "0.6.1"
|
||||
version = "1.3.0"
|
||||
description = "Python library for easily interacting with trained machine learning models"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "gradio_client-0.6.1-py3-none-any.whl", hash = "sha256:ee530fb3437f24943e51da70335909cc9b4f1e3e1ab1d219071b8ce9c1969777"},
|
||||
{file = "gradio_client-0.6.1.tar.gz", hash = "sha256:2f36a8467622f748539c84d142f1a71df6b83666d017ee4fb6ef8a2696f70f66"},
|
||||
{file = "gradio_client-1.3.0-py3-none-any.whl", hash = "sha256:20c40cb4d56e18de1a025ccf58079f08a304e4fb2dfbcf7c2352815b2cb31091"},
|
||||
{file = "gradio_client-1.3.0.tar.gz", hash = "sha256:d904afeae4f5682add0a6a263542c10e7669ff6c9de0a53a5c2fc9b719a24bb8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
fsspec = "*"
|
||||
httpx = "*"
|
||||
huggingface-hub = ">=0.13.0"
|
||||
httpx = ">=0.24.1"
|
||||
huggingface-hub = ">=0.19.3"
|
||||
packaging = "*"
|
||||
requests = ">=2.0,<3.0"
|
||||
typing-extensions = ">=4.0,<5.0"
|
||||
websockets = ">=10.0,<12.0"
|
||||
websockets = ">=10.0,<13.0"
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
@ -1450,43 +1417,6 @@ files = [
|
||||
{file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jsonschema"
|
||||
version = "4.23.0"
|
||||
description = "An implementation of JSON Schema validation for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
|
||||
{file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
attrs = ">=22.2.0"
|
||||
jsonschema-specifications = ">=2023.03.6"
|
||||
referencing = ">=0.28.4"
|
||||
rpds-py = ">=0.7.1"
|
||||
|
||||
[package.extras]
|
||||
format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
|
||||
format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "jsonschema-specifications"
|
||||
version = "2024.10.1"
|
||||
description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
|
||||
{file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
referencing = ">=0.31.0"
|
||||
|
||||
[[package]]
|
||||
name = "kiwisolver"
|
||||
version = "1.4.5"
|
||||
@ -1871,6 +1801,32 @@ files = [
|
||||
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
|
||||
testing = ["coverage", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "markdown-it-py"
|
||||
version = "3.0.0"
|
||||
description = "Python port of markdown-it. Markdown parsing, done right!"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"emscripten\""
|
||||
files = [
|
||||
{file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
|
||||
{file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
mdurl = ">=0.1,<1.0"
|
||||
|
||||
[package.extras]
|
||||
benchmarking = ["psutil", "pytest", "pytest-benchmark"]
|
||||
code-style = ["pre-commit (>=3.0,<4.0)"]
|
||||
compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
|
||||
linkify = ["linkify-it-py (>=1,<3)"]
|
||||
plugins = ["mdit-py-plugins"]
|
||||
profiling = ["gprof2dot"]
|
||||
rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
|
||||
testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
|
||||
|
||||
[[package]]
|
||||
name = "markupsafe"
|
||||
version = "2.1.5"
|
||||
@ -2009,6 +1965,19 @@ files = [
|
||||
[package.dependencies]
|
||||
traitlets = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mdurl"
|
||||
version = "0.1.2"
|
||||
description = "Markdown URL utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"emscripten\""
|
||||
files = [
|
||||
{file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
|
||||
{file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mpmath"
|
||||
version = "1.3.0"
|
||||
@ -2113,36 +2082,6 @@ files = [
|
||||
{file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "narwhals"
|
||||
version = "1.31.0"
|
||||
description = "Extremely lightweight compatibility layer between dataframe libraries"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "narwhals-1.31.0-py3-none-any.whl", hash = "sha256:2a7b79bb5f511055c4c0142121fc0d4171ea171458e12d44dbd9c8fc6488e997"},
|
||||
{file = "narwhals-1.31.0.tar.gz", hash = "sha256:333472e2562343dfdd27407ec9b5114a07c81d0416794e4ac6b703dd925c6a1a"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
core = ["duckdb", "pandas", "polars", "pyarrow", "sqlframe"]
|
||||
cudf = ["cudf (>=24.10.0)"]
|
||||
dask = ["dask[dataframe] (>=2024.8)"]
|
||||
dev = ["covdefaults", "hypothesis", "mypy (>=1.15.0,<1.16.0)", "pandas-stubs (==2.2.3.250308)", "polars (==1.25.2)", "pre-commit", "pyarrow-stubs (==17.18)", "pyright", "pytest", "pytest-cov", "pytest-env", "pytest-randomly", "sqlframe (==3.24.1)", "typing-extensions", "uv"]
|
||||
docs = ["black", "duckdb", "jinja2", "markdown-exec[ansi]", "mkdocs", "mkdocs-autorefs", "mkdocs-material", "mkdocstrings-python (>=1.16)", "mkdocstrings[python]", "pandas", "polars (>=1.0.0)", "pyarrow"]
|
||||
duckdb = ["duckdb (>=1.0)"]
|
||||
extra = ["scikit-learn"]
|
||||
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||||
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||||
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|
||||
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||||
|
||||
[[package]]
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||||
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[package.dependencies]
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rpds-py = ">=0.7.0"
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[[package]]
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name = "regex"
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version = "2024.7.24"
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@ -3404,116 +3409,52 @@ docs = ["numpydoc", "sphinx (!=1.3.1)"]
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tests = ["pytest (<8)", "pytest-cov", "scipy (>=1.1)"]
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[[package]]
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name = "rich"
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optional = false
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]
|
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|
||||
[package.dependencies]
|
||||
markdown-it-py = ">=2.2.0"
|
||||
pygments = ">=2.13.0,<3.0.0"
|
||||
|
||||
[package.extras]
|
||||
jupyter = ["ipywidgets (>=7.5.1,<9)"]
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.11.2"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"emscripten\""
|
||||
files = [
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{file = "ruff-0.11.2-py3-none-linux_armv6l.whl", hash = "sha256:c69e20ea49e973f3afec2c06376eb56045709f0212615c1adb0eda35e8a4e477"},
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{file = "ruff-0.11.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6e8fb75e14560f7cf53b15bbc55baf5ecbe373dd5f3aab96ff7aa7777edd7630"},
|
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{file = "ruff-0.11.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:842a472d7b4d6f5924e9297aa38149e5dcb1e628773b70e6387ae2c97a63c58f"},
|
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{file = "ruff-0.11.2-py3-none-win32.whl", hash = "sha256:aca01ccd0eb5eb7156b324cfaa088586f06a86d9e5314b0eb330cb48415097cc"},
|
||||
{file = "ruff-0.11.2-py3-none-win_amd64.whl", hash = "sha256:3170150172a8f994136c0c66f494edf199a0bbea7a409f649e4bc8f4d7084080"},
|
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{file = "ruff-0.11.2-py3-none-win_arm64.whl", hash = "sha256:52933095158ff328f4c77af3d74f0379e34fd52f175144cefc1b192e7ccd32b4"},
|
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{file = "ruff-0.11.2.tar.gz", hash = "sha256:ec47591497d5a1050175bdf4e1a4e6272cddff7da88a2ad595e1e326041d8d94"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -3700,6 +3641,19 @@ core = ["importlib-metadata (>=6) ; python_version < \"3.10\"", "importlib-resou
|
||||
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
|
||||
test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-ruff (<0.4) ; platform_system == \"Windows\"", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "pytest-ruff (>=0.3.2) ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
|
||||
|
||||
[[package]]
|
||||
name = "shellingham"
|
||||
version = "1.5.4"
|
||||
description = "Tool to Detect Surrounding Shell"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"emscripten\""
|
||||
files = [
|
||||
{file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
|
||||
{file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.16.0"
|
||||
@ -3788,21 +3742,21 @@ test = ["pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.22.0"
|
||||
version = "0.46.1"
|
||||
description = "The little ASGI library that shines."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "starlette-0.22.0-py3-none-any.whl", hash = "sha256:b5eda991ad5f0ee5d8ce4c4540202a573bb6691ecd0c712262d0bc85cf8f2c50"},
|
||||
{file = "starlette-0.22.0.tar.gz", hash = "sha256:b092cbc365bea34dd6840b42861bdabb2f507f8671e642e8272d2442e08ea4ff"},
|
||||
{file = "starlette-0.46.1-py3-none-any.whl", hash = "sha256:77c74ed9d2720138b25875133f3a2dae6d854af2ec37dceb56aef370c1d8a227"},
|
||||
{file = "starlette-0.46.1.tar.gz", hash = "sha256:3c88d58ee4bd1bb807c0d1acb381838afc7752f9ddaec81bbe4383611d833230"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.4.0,<5"
|
||||
anyio = ">=3.6.2,<5"
|
||||
|
||||
[package.extras]
|
||||
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
|
||||
full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
@ -3935,6 +3889,18 @@ dev = ["tokenizers[testing]"]
|
||||
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
|
||||
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
|
||||
|
||||
[[package]]
|
||||
name = "tomlkit"
|
||||
version = "0.12.0"
|
||||
description = "Style preserving TOML library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "tomlkit-0.12.0-py3-none-any.whl", hash = "sha256:926f1f37a1587c7a4f6c7484dae538f1345d96d793d9adab5d3675957b1d0766"},
|
||||
{file = "tomlkit-0.12.0.tar.gz", hash = "sha256:01f0477981119c7d8ee0f67ebe0297a7c95b14cf9f4b102b45486deb77018716"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.4.1"
|
||||
@ -4255,6 +4221,25 @@ build = ["cmake (>=3.20)", "lit"]
|
||||
tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
|
||||
tutorials = ["matplotlib", "pandas", "tabulate"]
|
||||
|
||||
[[package]]
|
||||
name = "typer"
|
||||
version = "0.15.2"
|
||||
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "sys_platform != \"emscripten\""
|
||||
files = [
|
||||
{file = "typer-0.15.2-py3-none-any.whl", hash = "sha256:46a499c6107d645a9c13f7ee46c5d5096cae6f5fc57dd11eccbbb9ae3e44ddfc"},
|
||||
{file = "typer-0.15.2.tar.gz", hash = "sha256:ab2fab47533a813c49fe1f16b1a370fd5819099c00b119e0633df65f22144ba5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
click = ">=8.0.0"
|
||||
rich = ">=10.11.0"
|
||||
shellingham = ">=1.3.0"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
@ -4417,4 +4402,4 @@ watchdog = ["watchdog (>=2.3)"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.11,<3.12"
|
||||
content-hash = "e73427a82e6d9b8531424dc6552247cca063044c9bd2aa9410f2a37bee9e8d3b"
|
||||
content-hash = "fe6e4ead12704e8271f386cd43b56682ede6d75116724dd89db5ca443489f163"
|
||||
|
||||
@ -46,7 +46,7 @@ numpy = ">=1.0,<2.0"
|
||||
scipy = ">=1.9.0"
|
||||
librosa = "==0.10.2"
|
||||
faiss-cpu = ">=1.7.0"
|
||||
gradio = "==3.50.0"
|
||||
gradio = "4.44.1"
|
||||
soundfile = "0.12.1"
|
||||
ffmpeg-python = ">=0.2.0"
|
||||
matplotlib = ">=3.7.0"
|
||||
@ -62,7 +62,7 @@ pyworld = "==0.3.2"
|
||||
onnxruntime = { version = ">=1.13.0", markers = "sys_platform == 'darwin'" }
|
||||
onnxruntime-gpu = { version = ">=1.13.0", markers = "sys_platform != 'darwin'" }
|
||||
torchcrepe = "==0.0.23"
|
||||
fastapi = "==0.88"
|
||||
fastapi = "*"
|
||||
torchfcpe = ">=0.0.1"
|
||||
ffmpy = "==0.3.1"
|
||||
python-dotenv = ">=1.0.0"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user