From 0f3261e0717be9e3a59b652fea7c1a388c8fa43a Mon Sep 17 00:00:00 2001 From: mrq Date: Sun, 5 Mar 2023 14:03:18 +0000 Subject: [PATCH] you should have migrated by now, if anything breaks it's on (You) --- README.md | 6 +- list_devices.py | 5 - main.py | 36 -- setup-cuda.bat | 8 - setup-cuda.sh | 8 - setup-directml.bat | 8 - setup-rocm.sh | 8 - start.bat | 4 - start.sh | 3 - tortoise_tts.ipynb | 137 ------ update-force.bat | 3 - update-force.sh | 3 - update.bat | 7 - update.sh | 6 - voices/.gitkeep | 0 webui.py | 1103 -------------------------------------------- 16 files changed, 4 insertions(+), 1341 deletions(-) delete mode 100755 list_devices.py delete mode 100755 main.py delete mode 100755 setup-cuda.bat delete mode 100755 setup-cuda.sh delete mode 100755 setup-directml.bat delete mode 100755 setup-rocm.sh delete mode 100755 start.bat delete mode 100755 start.sh delete mode 100755 tortoise_tts.ipynb delete mode 100755 update-force.bat delete mode 100755 update-force.sh delete mode 100755 update.bat delete mode 100755 update.sh delete mode 100755 voices/.gitkeep delete mode 100755 webui.py diff --git a/README.md b/README.md index 26781af..1879920 100755 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # (QoL improvements for) TorToiSe -This repo is for my modifications to [neonbjb/tortoise-tts](https://github.com/neonbjb/tortoise-tts). +This repo is for my modifications to [neonbjb/tortoise-tts](https://github.com/neonbjb/tortoise-tts). If you need the original README, refer to the original repo. -Please migrate to [mrq/ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning) for future additions. \ No newline at end of file +\> w-where'd everything go? + +Please migrate to [mrq/ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning), as that repo is the more cohesive package for voice cloning. \ No newline at end of file diff --git a/list_devices.py b/list_devices.py deleted file mode 100755 index 1a35ad5..0000000 --- a/list_devices.py +++ /dev/null @@ -1,5 +0,0 @@ -import torch - -devices = [f"cuda:{i} => {torch.cuda.get_device_name(i)}" for i in range(torch.cuda.device_count())] - -print(devices) \ No newline at end of file diff --git a/main.py b/main.py deleted file mode 100755 index 947b81c..0000000 --- a/main.py +++ /dev/null @@ -1,36 +0,0 @@ -import os -import webui as mrq - -print('DEPRECATION WARNING: this repo has been refractored to focus entirely on tortoise-tts. Please migrate to https://git.ecker.tech/mrq/ai-voice-cloning if you seek new features.') - -if 'TORTOISE_MODELS_DIR' not in os.environ: - os.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/')) - -if 'TRANSFORMERS_CACHE' not in os.environ: - os.environ['TRANSFORMERS_CACHE'] = os.path.realpath(os.path.join(os.getcwd(), './models/transformers/')) - -if __name__ == "__main__": - mrq.args = mrq.setup_args() - - if mrq.args.listen_path is not None and mrq.args.listen_path != "/": - import uvicorn - uvicorn.run("main:app", host=mrq.args.listen_host, port=mrq.args.listen_port if not None else 8000) - else: - mrq.webui = mrq.setup_gradio() - mrq.webui.launch(share=mrq.args.share, prevent_thread_lock=True, server_name=mrq.args.listen_host, server_port=mrq.args.listen_port) - mrq.tts = mrq.setup_tortoise() - - mrq.webui.block_thread() -elif __name__ == "main": - from fastapi import FastAPI - import gradio as gr - - import sys - sys.argv = [sys.argv[0]] - - app = FastAPI() - mrq.args = mrq.setup_args() - mrq.webui = mrq.setup_gradio() - app = gr.mount_gradio_app(app, mrq.webui, path=mrq.args.listen_path) - - mrq.tts = mrq.setup_tortoise() diff --git a/setup-cuda.bat b/setup-cuda.bat deleted file mode 100755 index e32dc50..0000000 --- a/setup-cuda.bat +++ /dev/null @@ -1,8 +0,0 @@ -python -m venv tortoise-venv -call .\tortoise-venv\Scripts\activate.bat -python -m pip install --upgrade pip -python -m pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 -python -m pip install -r ./requirements.txt -python -m pip install -r ./requirements_legacy.txt -deactivate -pause \ No newline at end of file diff --git a/setup-cuda.sh b/setup-cuda.sh deleted file mode 100755 index 11d1943..0000000 --- a/setup-cuda.sh +++ /dev/null @@ -1,8 +0,0 @@ -python -m venv tortoise-venv -source ./tortoise-venv/bin/activate -python -m pip install --upgrade pip -# CUDA -pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 -python -m pip install -r ./requirements.txt -python -m pip install -r ./requirements_legacy.txt -deactivate diff --git a/setup-directml.bat b/setup-directml.bat deleted file mode 100755 index c4608d2..0000000 --- a/setup-directml.bat +++ /dev/null @@ -1,8 +0,0 @@ -python -m venv tortoise-venv -call .\tortoise-venv\Scripts\activate.bat -python -m pip install --upgrade pip -python -m pip install torch torchvision torchaudio torch-directml -python -m pip install -r ./requirements.txt -python -m pip install -r ./requirements_legacy.txt -deactivate -pause \ No newline at end of file diff --git a/setup-rocm.sh b/setup-rocm.sh deleted file mode 100755 index 8e7a59c..0000000 --- a/setup-rocm.sh +++ /dev/null @@ -1,8 +0,0 @@ -python -m venv tortoise-venv -source ./tortoise-venv/bin/activate -python -m pip install --upgrade pip -# ROCM -pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.1.1 # 5.2 does not work for me desu -python -m pip install -r ./requirements.txt -python -m pip install -r ./requirements_legacy.txt -deactivate diff --git a/start.bat b/start.bat deleted file mode 100755 index 4cbd131..0000000 --- a/start.bat +++ /dev/null @@ -1,4 +0,0 @@ -call .\tortoise-venv\Scripts\activate.bat -python main.py -deactivate -pause \ No newline at end of file diff --git a/start.sh b/start.sh deleted file mode 100755 index b67f402..0000000 --- a/start.sh +++ /dev/null @@ -1,3 +0,0 @@ -source ./tortoise-venv/bin/activate -python3 ./main.py -deactivate diff --git a/tortoise_tts.ipynb b/tortoise_tts.ipynb deleted file mode 100755 index 3f501a5..0000000 --- a/tortoise_tts.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "nbformat":4, - "nbformat_minor":0, - "metadata":{ - "colab":{ - "private_outputs":true, - "provenance":[ - - ] - }, - "kernelspec":{ - "name":"python3", - "display_name":"Python 3" - }, - "language_info":{ - "name":"python" - }, - "accelerator":"GPU", - "gpuClass":"standard" - }, - "cells":[ - { - "cell_type":"markdown", - "source":[ - "## Initialization" - ], - "metadata":{ - "id":"ni41hmE03DL6" - } - }, - { - "cell_type":"code", - "execution_count":null, - "metadata":{ - "id":"FtsMKKfH18iM" - }, - "outputs":[ - - ], - "source":[ - "!git clone https://git.ecker.tech/mrq/ai-voice-cloning/\n", - "%cd ai-voice-cloning\n", - "!python -m pip install --upgrade pip\n", - "!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n", - "!python -m pip install -r ./requirements.txt" - ] - }, - { - "cell_type":"code", - "source":[ - "# colab requires the runtime to restart before use\n", - "exit()" - ], - "metadata":{ - "id":"FVUOtSASCSJ8" - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":[ - "## Running" - ], - "metadata":{ - "id":"o1gkfw3B3JSk" - } - }, - { - "cell_type":"code", - "source":[ - "%cd /content/ai-voice-cloning\n", - "\n", - "import os\n", - "import sys\n", - "\n", - "sys.argv = [\"\"]\n", - "sys.path.append('./src/')\n", - "\n", - "if 'TORTOISE_MODELS_DIR' not in os.environ:\n", - "\tos.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/'))\n", - "\n", - "if 'TRANSFORMERS_CACHE' not in os.environ:\n", - "\tos.environ['TRANSFORMERS_CACHE'] = os.path.realpath(os.path.join(os.getcwd(), './models/transformers/'))\n", - "\n", - "from utils import *\n", - "from webui import *\n", - "\n", - "args = setup_args()\n", - "\n", - "webui = setup_gradio()\n", - "tts = setup_tortoise()\n", - "webui.launch(share=True, prevent_thread_lock=True, height=1000)\n", - "webui.block_thread()" - ], - "metadata":{ - "id":"c_EQZLTA19c7" - }, - "execution_count":null, - "outputs":[ - - ] - }, - { - "cell_type":"markdown", - "source":[ - "## Exporting" - ], - "metadata":{ - "id":"2AnVQxEJx47p" - } - }, - { - "cell_type":"code", - "source":[ - "%cd /content/ai-voice-cloning\n", - "!apt install -y p7zip-full\n", - "from datetime import datetime\n", - "timestamp = datetime.now().strftime('%m-%d-%Y_%H:%M:%S')\n", - "!mkdir -p \"../{timestamp}\"\n", - "!mv ./results/* \"../{timestamp}/.\"\n", - "!7z a -t7z -m0=lzma2 -mx=9 -mfb=64 -md=32m -ms=on \"../{timestamp}.7z\" \"../{timestamp}/\"\n", - "!ls ~/\n", - "!echo \"Finished zipping, archive is available at {timestamp}.7z\"" - ], - "metadata":{ - "id":"YOACiDCXx72G" - }, - "execution_count":null, - "outputs":[ - - ] - } - ] -} \ No newline at end of file diff --git a/update-force.bat b/update-force.bat deleted file mode 100755 index 0984917..0000000 --- a/update-force.bat +++ /dev/null @@ -1,3 +0,0 @@ -git fetch --all -git reset --hard origin/main -call .\update.bat \ No newline at end of file diff --git a/update-force.sh b/update-force.sh deleted file mode 100755 index 4db8623..0000000 --- a/update-force.sh +++ /dev/null @@ -1,3 +0,0 @@ -git fetch --all -git reset --hard origin/main -./update.sh \ No newline at end of file diff --git a/update.bat b/update.bat deleted file mode 100755 index 699cd5f..0000000 --- a/update.bat +++ /dev/null @@ -1,7 +0,0 @@ -git pull -python -m venv tortoise-venv -call .\tortoise-venv\Scripts\activate.bat -python -m pip install --upgrade pip -python -m pip install -r ./requirements.txt -deactivate -pause \ No newline at end of file diff --git a/update.sh b/update.sh deleted file mode 100755 index e3cfb09..0000000 --- a/update.sh +++ /dev/null @@ -1,6 +0,0 @@ -git pull -python -m venv tortoise-venv -source ./tortoise-venv/bin/activate -python -m pip install --upgrade pip -python -m pip install -r ./requirements.txt -deactivate \ No newline at end of file diff --git a/voices/.gitkeep b/voices/.gitkeep deleted file mode 100755 index e69de29..0000000 diff --git a/webui.py b/webui.py deleted file mode 100755 index b845531..0000000 --- a/webui.py +++ /dev/null @@ -1,1103 +0,0 @@ -import os -import argparse -import time -import json -import base64 -import re -import urllib.request - -import torch -import torchaudio -import music_tag -import gradio as gr -import gradio.utils - -from datetime import datetime - -import tortoise.api - -from tortoise.api import TextToSpeech -from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir -from tortoise.utils.text import split_and_recombine_text -from tortoise.utils.device import get_device_name, set_device_name - -voicefixer = None - -def generate( - text, - delimiter, - emotion, - prompt, - voice, - mic_audio, - voice_latents_chunks, - seed, - candidates, - num_autoregressive_samples, - diffusion_iterations, - temperature, - diffusion_sampler, - breathing_room, - cvvp_weight, - top_p, - diffusion_temperature, - length_penalty, - repetition_penalty, - cond_free_k, - experimental_checkboxes, - progress=None -): - global args - global tts - - try: - tts - except NameError: - raise gr.Error("TTS is still initializing...") - - if voice != "microphone": - voices = [voice] - else: - voices = [] - - if voice == "microphone": - if mic_audio is None: - raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input") - mic = load_audio(mic_audio, tts.input_sample_rate) - voice_samples, conditioning_latents = [mic], None - elif voice == "random": - voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents() - else: - progress(0, desc="Loading voice...") - voice_samples, conditioning_latents = load_voice(voice) - - if voice_samples is not None: - sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu() - - conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents) - if len(conditioning_latents) == 4: - conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) - - if voice != "microphone": - torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth') - voice_samples = None - else: - if conditioning_latents is not None: - sample_voice, _ = load_voice(voice, load_latents=False) - sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu() - else: - sample_voice = None - - if seed == 0: - seed = None - - if conditioning_latents is not None and len(conditioning_latents) == 2 and cvvp_weight > 0: - print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.") - cvvp_weight = 0 - - - settings = { - 'temperature': float(temperature), - - 'top_p': float(top_p), - 'diffusion_temperature': float(diffusion_temperature), - 'length_penalty': float(length_penalty), - 'repetition_penalty': float(repetition_penalty), - 'cond_free_k': float(cond_free_k), - - 'num_autoregressive_samples': num_autoregressive_samples, - 'sample_batch_size': args.sample_batch_size, - 'diffusion_iterations': diffusion_iterations, - - 'voice_samples': voice_samples, - 'conditioning_latents': conditioning_latents, - 'use_deterministic_seed': seed, - 'return_deterministic_state': True, - 'k': candidates, - 'diffusion_sampler': diffusion_sampler, - 'breathing_room': breathing_room, - 'progress': progress, - 'half_p': "Half Precision" in experimental_checkboxes, - 'cond_free': "Conditioning-Free" in experimental_checkboxes, - 'cvvp_amount': cvvp_weight, - } - - if delimiter == "\\n": - delimiter = "\n" - - if delimiter != "" and delimiter in text: - texts = text.split(delimiter) - else: - texts = split_and_recombine_text(text) - - full_start_time = time.time() - - outdir = f"./results/{voice}/" - os.makedirs(outdir, exist_ok=True) - - audio_cache = {} - - resample = None - # not a ternary in the event for some reason I want to rely on librosa's upsampling interpolator rather than torchaudio's, for some reason - if tts.output_sample_rate != args.output_sample_rate: - resampler = torchaudio.transforms.Resample( - tts.output_sample_rate, - args.output_sample_rate, - lowpass_filter_width=16, - rolloff=0.85, - resampling_method="kaiser_window", - beta=8.555504641634386, - ) - - volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None - - idx = 0 - idx_cache = {} - for i, file in enumerate(os.listdir(outdir)): - filename = os.path.basename(file) - extension = os.path.splitext(filename)[1] - if extension != ".json" and extension != ".wav": - continue - match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename) - - key = int(match[0]) - idx_cache[key] = True - - if len(idx_cache) > 0: - keys = sorted(list(idx_cache.keys())) - idx = keys[-1] + 1 - - # I know there's something to pad I don't care - pad = "" - for i in range(4,0,-1): - if idx < 10 ** i: - pad = f"{pad}0" - idx = f"{pad}{idx}" - - def get_name(line=0, candidate=0, combined=False): - name = f"{idx}" - if combined: - name = f"{name}_combined" - elif len(texts) > 1: - name = f"{name}_{line}" - if candidates > 1: - name = f"{name}_{candidate}" - return name - - for line, cut_text in enumerate(texts): - if emotion == "Custom": - if prompt.strip() != "": - cut_text = f"[{prompt},] {cut_text}" - else: - cut_text = f"[I am really {emotion.lower()},] {cut_text}" - - progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]' - print(f"{progress.msg_prefix} Generating line: {cut_text}") - - start_time = time.time() - gen, additionals = tts.tts(cut_text, **settings ) - seed = additionals[0] - run_time = time.time()-start_time - print(f"Generating line took {run_time} seconds") - - if not isinstance(gen, list): - gen = [gen] - - for j, g in enumerate(gen): - audio = g.squeeze(0).cpu() - name = get_name(line=line, candidate=j) - audio_cache[name] = { - 'audio': audio, - 'text': cut_text, - 'time': run_time - } - # save here in case some error happens mid-batch - torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate) - - for k in audio_cache: - audio = audio_cache[k]['audio'] - - if resampler is not None: - audio = resampler(audio) - if volume_adjust is not None: - audio = volume_adjust(audio) - - audio_cache[k]['audio'] = audio - torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate) - - output_voices = [] - for candidate in range(candidates): - if len(texts) > 1: - audio_clips = [] - for line in range(len(texts)): - name = get_name(line=line, candidate=candidate) - audio = audio_cache[name]['audio'] - audio_clips.append(audio) - - name = get_name(candidate=candidate, combined=True) - audio = torch.cat(audio_clips, dim=-1) - torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate) - - audio = audio.squeeze(0).cpu() - audio_cache[name] = { - 'audio': audio, - 'text': text, - 'time': time.time()-full_start_time, - 'output': True - } - else: - name = get_name(candidate=candidate) - audio_cache[name]['output'] = True - - info = { - 'text': text, - 'delimiter': '\\n' if delimiter == "\n" else delimiter, - 'emotion': emotion, - 'prompt': prompt, - 'voice': voice, - 'seed': seed, - 'candidates': candidates, - 'num_autoregressive_samples': num_autoregressive_samples, - 'diffusion_iterations': diffusion_iterations, - 'temperature': temperature, - 'diffusion_sampler': diffusion_sampler, - 'breathing_room': breathing_room, - 'cvvp_weight': cvvp_weight, - 'top_p': top_p, - 'diffusion_temperature': diffusion_temperature, - 'length_penalty': length_penalty, - 'repetition_penalty': repetition_penalty, - 'cond_free_k': cond_free_k, - 'experimentals': experimental_checkboxes, - 'time': time.time()-full_start_time, - } - - # kludgy yucky codesmells - for name in audio_cache: - if 'output' not in audio_cache[name]: - continue - - output_voices.append(f'{outdir}/{voice}_{name}.wav') - with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f: - f.write(json.dumps(info, indent='\t') ) - - if args.voice_fixer and voicefixer: - fixed_output_voices = [] - for path in progress.tqdm(output_voices, desc="Running voicefix..."): - fixed = path.replace(".wav", "_fixed.wav") - voicefixer.restore( - input=path, - output=fixed, - cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda, - #mode=mode, - ) - fixed_output_voices.append(fixed) - output_voices = fixed_output_voices - - if voice is not None and conditioning_latents is not None: - with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f: - info['latents'] = base64.b64encode(f.read()).decode("ascii") - - if args.embed_output_metadata: - for name in progress.tqdm(audio_cache, desc="Embedding metadata..."): - info['text'] = audio_cache[name]['text'] - info['time'] = audio_cache[name]['time'] - - metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav") - metadata['lyrics'] = json.dumps(info) - metadata.save() - - if sample_voice is not None: - sample_voice = (tts.input_sample_rate, sample_voice.numpy()) - - print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n") - - info['seed'] = settings['use_deterministic_seed'] - if 'latents' in info: - del info['latents'] - - with open(f'./config/generate.json', 'w', encoding="utf-8") as f: - f.write(json.dumps(info, indent='\t') ) - - stats = [ - [ seed, "{:.3f}".format(info['time']) ] - ] - - return ( - sample_voice, - output_voices, - stats, - ) - -def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)): - global tts - try: - tts - except NameError: - raise gr.Error("TTS is still initializing...") - - voice_samples, conditioning_latents = load_voice(voice, load_latents=False) - - if voice_samples is None: - return - - conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents) - - if len(conditioning_latents) == 4: - conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) - - torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth') - - return voice - -def update_presets(value): - PRESETS = { - 'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False}, - 'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80}, - 'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200}, - 'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400}, - } - - if value in PRESETS: - preset = PRESETS[value] - return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations'])) - else: - return (gr.update(), gr.update()) - -def read_generate_settings(file, read_latents=True, read_json=True): - j = None - latents = None - - if file is not None: - if hasattr(file, 'name'): - file = file.name - - if file[-4:] == ".wav": - metadata = music_tag.load_file(file) - if 'lyrics' in metadata: - j = json.loads(str(metadata['lyrics'])) - elif file[-5:] == ".json": - with open(file, 'r') as f: - j = json.load(f) - - if j is None: - gr.Error("No metadata found in audio file to read") - else: - if 'latents' in j: - if read_latents: - latents = base64.b64decode(j['latents']) - del j['latents'] - - - if "time" in j: - j["time"] = "{:.3f}".format(j["time"]) - - return ( - j, - latents, - ) - -def import_voice(file, saveAs = None): - j, latents = read_generate_settings(file, read_latents=True) - - if j is not None and saveAs is None: - saveAs = j['voice'] - if saveAs is None or saveAs == "": - raise gr.Error("Specify a voice name") - - outdir = f'{get_voice_dir()}/{saveAs}/' - os.makedirs(outdir, exist_ok=True) - if latents: - with open(f'{outdir}/cond_latents.pth', 'wb') as f: - f.write(latents) - latents = f'{outdir}/cond_latents.pth' - print(f"Imported latents to {latents}") - else: - filename = file.name - if filename[-4:] != ".wav": - raise gr.Error("Please convert to a WAV first") - - path = f"{outdir}/{os.path.basename(filename)}" - waveform, sampling_rate = torchaudio.load(filename) - - if args.voice_fixer: - # resample to best bandwidth since voicefixer will do it anyways through librosa - if sampling_rate != 44100: - print(f"Resampling imported voice sample: {path}") - resampler = torchaudio.transforms.Resample( - sampling_rate, - 44100, - lowpass_filter_width=16, - rolloff=0.85, - resampling_method="kaiser_window", - beta=8.555504641634386, - ) - waveform = resampler(waveform) - sampling_rate = 44100 - - torchaudio.save(path, waveform, sampling_rate) - - print(f"Running 'voicefixer' on voice sample: {path}") - voicefixer.restore( - input = path, - output = path, - cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda, - #mode=mode, - ) - else: - torchaudio.save(path, waveform, sampling_rate) - - - print(f"Imported voice to {path}") - - -def import_generate_settings(file="./config/generate.json"): - settings, _ = read_generate_settings(file, read_latents=False) - - if settings is None: - return None - - return ( - None if 'text' not in settings else settings['text'], - None if 'delimiter' not in settings else settings['delimiter'], - None if 'emotion' not in settings else settings['emotion'], - None if 'prompt' not in settings else settings['prompt'], - None if 'voice' not in settings else settings['voice'], - None, - None, - None if 'seed' not in settings else settings['seed'], - None if 'candidates' not in settings else settings['candidates'], - None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'], - None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'], - 0.8 if 'temperature' not in settings else settings['temperature'], - "DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'], - 8 if 'breathing_room' not in settings else settings['breathing_room'], - 0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'], - 0.8 if 'top_p' not in settings else settings['top_p'], - 1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'], - 1.0 if 'length_penalty' not in settings else settings['length_penalty'], - 2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'], - 2.0 if 'cond_free_k' not in settings else settings['cond_free_k'], - None if 'experimentals' not in settings else settings['experimentals'], - ) - -def curl(url): - try: - req = urllib.request.Request(url, headers={'User-Agent': 'Python'}) - conn = urllib.request.urlopen(req) - data = conn.read() - data = data.decode() - data = json.loads(data) - conn.close() - return data - except Exception as e: - print(e) - return None - -def check_for_updates(): - if not os.path.isfile('./.git/FETCH_HEAD'): - print("Cannot check for updates: not from a git repo") - return False - - with open(f'./.git/FETCH_HEAD', 'r', encoding="utf-8") as f: - head = f.read() - - match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head) - if match is None or len(match) == 0: - print("Cannot check for updates: cannot parse FETCH_HEAD") - return False - - match = match[0] - - local = match[0] - host = match[1] - owner = match[2] - repo = match[3] - - res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances - - if res is None or len(res) == 0: - print("Cannot check for updates: cannot fetch from remote") - return False - - remote = res[0]["commit"]["id"] - - if remote != local: - print(f"New version found: {local[:8]} => {remote[:8]}") - return True - - return False - -def reload_tts(): - global tts - del tts - tts = setup_tortoise(restart=True) - -def cancel_generate(): - tortoise.api.STOP_SIGNAL = True - -def get_voice_list(dir=get_voice_dir()): - os.makedirs(dir, exist_ok=True) - return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"] - -def update_voices(): - return ( - gr.Dropdown.update(choices=get_voice_list()), - gr.Dropdown.update(choices=get_voice_list("./results/")), - ) - -def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): - args.listen = listen - args.share = share - args.check_for_updates = check_for_updates - args.models_from_local_only = models_from_local_only - args.low_vram = low_vram - args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents - args.device_override = device_override - args.sample_batch_size = sample_batch_size - args.embed_output_metadata = embed_output_metadata - args.latents_lean_and_mean = latents_lean_and_mean - args.voice_fixer = voice_fixer - args.voice_fixer_use_cuda = voice_fixer_use_cuda - args.concurrency_count = concurrency_count - args.output_sample_rate = output_sample_rate - args.output_volume = output_volume - - settings = { - 'listen': None if args.listen else args.listen, - 'share': args.share, - 'low-vram':args.low_vram, - 'check-for-updates':args.check_for_updates, - 'models-from-local-only':args.models_from_local_only, - 'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents, - 'device-override': args.device_override, - 'sample-batch-size': args.sample_batch_size, - 'embed-output-metadata': args.embed_output_metadata, - 'latents-lean-and-mean': args.latents_lean_and_mean, - 'voice-fixer': args.voice_fixer, - 'voice-fixer-use-cuda': args.voice_fixer_use_cuda, - 'concurrency-count': args.concurrency_count, - 'output-sample-rate': args.output_sample_rate, - 'output-volume': args.output_volume, - } - - with open(f'./config/exec.json', 'w', encoding="utf-8") as f: - f.write(json.dumps(settings, indent='\t') ) - -def setup_args(): - default_arguments = { - 'share': False, - 'listen': None, - 'check-for-updates': False, - 'models-from-local-only': False, - 'low-vram': False, - 'sample-batch-size': None, - 'embed-output-metadata': True, - 'latents-lean-and-mean': True, - 'voice-fixer': True, - 'voice-fixer-use-cuda': True, - 'force-cpu-for-conditioning-latents': False, - 'device-override': None, - 'concurrency-count': 2, - 'output-sample-rate': 44100, - 'output-volume': 1, - } - - if os.path.isfile('./config/exec.json'): - with open(f'./config/exec.json', 'r', encoding="utf-8") as f: - overrides = json.load(f) - for k in overrides: - default_arguments[k] = overrides[k] - - parser = argparse.ArgumentParser() - parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere") - parser.add_argument("--listen", default=default_arguments['listen'], help="Path for Gradio to listen on") - parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup") - parser.add_argument("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models") - parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage") - parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)") - parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.") - parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.") - parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.") - parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)") - parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") - parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass") - parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once") - parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)") - parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output") - args = parser.parse_args() - - args.embed_output_metadata = not args.no_embed_output_metadata - - set_device_name(args.device_override) - - args.listen_host = None - args.listen_port = None - args.listen_path = None - if args.listen: - try: - match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0] - - args.listen_host = match[0] if match[0] != "" else "127.0.0.1" - args.listen_port = match[1] if match[1] != "" else None - args.listen_path = match[2] if match[2] != "" else "/" - except Exception as e: - pass - - if args.listen_port is not None: - args.listen_port = int(args.listen_port) - - return args - -def setup_tortoise(restart=False): - global args - global tts - global voicefixer - - if args.voice_fixer and not restart: - try: - from voicefixer import VoiceFixer - print("Initializating voice-fixer") - voicefixer = VoiceFixer() - print("initialized voice-fixer") - except Exception as e: - print(f"Error occurred while tring to initialize voicefixer: {e}") - - print("Initializating TorToiSe...") - tts = TextToSpeech(minor_optimizations=not args.low_vram) - print("TorToiSe initialized, ready for generation.") - return tts - -def setup_gradio(): - global args - - if not args.share: - def noop(function, return_value=None): - def wrapped(*args, **kwargs): - return return_value - return wrapped - gradio.utils.version_check = noop(gradio.utils.version_check) - gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics) - gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics) - gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics) - gradio.utils.error_analytics = noop(gradio.utils.error_analytics) - gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics) - #gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost') - - if args.models_from_local_only: - os.environ['TRANSFORMERS_OFFLINE']='1' - - with gr.Blocks() as webui: - with gr.Tab("Generate"): - with gr.Row(): - with gr.Column(): - text = gr.Textbox(lines=4, label="Prompt") - with gr.Row(): - with gr.Column(): - delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n") - - emotion = gr.Radio( - ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], - value="Custom", - label="Emotion", - type="value", - interactive=True - ) - prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)") - voice = gr.Dropdown( - get_voice_list(), - label="Voice", - type="value", - ) - mic_audio = gr.Audio( - label="Microphone Source", - source="microphone", - type="filepath", - ) - refresh_voices = gr.Button(value="Refresh Voice List") - voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1) - recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents") - recompute_voice_latents.click(compute_latents, - inputs=[ - voice, - voice_latents_chunks, - ], - outputs=voice, - ) - - prompt.change(fn=lambda value: gr.update(value="Custom"), - inputs=prompt, - outputs=emotion - ) - mic_audio.change(fn=lambda value: gr.update(value="microphone"), - inputs=mic_audio, - outputs=voice - ) - with gr.Column(): - candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates") - seed = gr.Number(value=0, precision=0, label="Seed") - - preset = gr.Radio( - ["Ultra Fast", "Fast", "Standard", "High Quality"], - label="Preset", - type="value", - ) - num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples") - diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations") - - temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature") - breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size") - diffusion_sampler = gr.Radio( - ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"], - value="P", - label="Diffusion Samplers", - type="value", - ) - - preset.change(fn=update_presets, - inputs=preset, - outputs=[ - num_autoregressive_samples, - diffusion_iterations, - ], - ) - - show_experimental_settings = gr.Checkbox(label="Show Experimental Settings") - reset_generation_settings_button = gr.Button(value="Reset to Default") - with gr.Column(visible=False) as col: - experimental_column = col - - experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags") - cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight") - top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P") - diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature") - length_penalty = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty") - repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty") - cond_free_k = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K") - - show_experimental_settings.change( - fn=lambda x: gr.update(visible=x), - inputs=show_experimental_settings, - outputs=experimental_column - ) - with gr.Column(): - submit = gr.Button(value="Generate") - stop = gr.Button(value="Stop") - - generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False) - source_sample = gr.Audio(label="Source Sample", visible=False) - output_audio = gr.Audio(label="Output") - candidates_list = gr.Dropdown(label="Candidates", type="value", visible=False) - output_pick = gr.Button(value="Select Candidate", visible=False) - - with gr.Tab("History"): - with gr.Row(): - with gr.Column(): - headers = { - "Name": "", - "Samples": "num_autoregressive_samples", - "Iterations": "diffusion_iterations", - "Temp.": "temperature", - "Sampler": "diffusion_sampler", - "CVVP": "cvvp_weight", - "Top P": "top_p", - "Diff. Temp.": "diffusion_temperature", - "Len Pen": "length_penalty", - "Rep Pen": "repetition_penalty", - "Cond-Free K": "cond_free_k", - "Time": "time", - } - history_info = gr.Dataframe(label="Results", headers=list(headers.keys())) - with gr.Row(): - with gr.Column(): - history_voices = gr.Dropdown( - get_voice_list("./results/"), - label="Voice", - type="value", - ) - - history_view_results_button = gr.Button(value="View Files") - with gr.Column(): - history_results_list = gr.Dropdown(label="Results",type="value", interactive=True) - history_view_result_button = gr.Button(value="View File") - with gr.Column(): - history_audio = gr.Audio() - history_copy_settings_button = gr.Button(value="Copy Settings") - - def history_view_results( voice ): - results = [] - files = [] - outdir = f"./results/{voice}/" - for i, file in enumerate(sorted(os.listdir(outdir))): - if file[-4:] != ".wav": - continue - - metadata, _ = read_generate_settings(f"{outdir}/{file}", read_latents=False) - if metadata is None: - continue - - values = [] - for k in headers: - v = file - if k != "Name": - v = metadata[headers[k]] - values.append(v) - - - files.append(file) - results.append(values) - - return ( - results, - gr.Dropdown.update(choices=sorted(files)) - ) - - history_view_results_button.click( - fn=history_view_results, - inputs=history_voices, - outputs=[ - history_info, - history_results_list, - ] - ) - history_view_result_button.click( - fn=lambda voice, file: f"./results/{voice}/{file}", - inputs=[ - history_voices, - history_results_list, - ], - outputs=history_audio - ) - with gr.Tab("Utilities"): - with gr.Row(): - with gr.Column(): - audio_in = gr.File(type="file", label="Audio Input", file_types=["audio"]) - copy_button = gr.Button(value="Copy Settings") - import_voice_name = gr.Textbox(label="Voice Name") - import_voice_button = gr.Button(value="Import Voice") - with gr.Column(): - metadata_out = gr.JSON(label="Audio Metadata") - latents_out = gr.File(type="binary", label="Voice Latents") - - def read_generate_settings_proxy(file, saveAs='.temp'): - j, latents = read_generate_settings(file) - - if latents: - outdir = f'{get_voice_dir()}/{saveAs}/' - os.makedirs(outdir, exist_ok=True) - with open(f'{outdir}/cond_latents.pth', 'wb') as f: - f.write(latents) - - latents = f'{outdir}/cond_latents.pth' - - return ( - j, - gr.update(value=latents, visible=latents is not None), - None if j is None else j['voice'] - ) - - audio_in.upload( - fn=read_generate_settings_proxy, - inputs=audio_in, - outputs=[ - metadata_out, - latents_out, - import_voice_name - ] - ) - - import_voice_button.click( - fn=import_voice, - inputs=[ - audio_in, - import_voice_name, - ] - ) - with gr.Tab("Settings"): - with gr.Row(): - exec_inputs = [] - with gr.Column(): - exec_inputs = exec_inputs + [ - gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"), - gr.Checkbox(label="Public Share Gradio", value=args.share), - gr.Checkbox(label="Check For Updates", value=args.check_for_updates), - gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only), - gr.Checkbox(label="Low VRAM", value=args.low_vram), - gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata), - gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean), - gr.Checkbox(label="Voice Fixer", value=args.voice_fixer), - gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda), - gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents), - gr.Textbox(label="Device Override", value=args.device_override), - ] - gr.Button(value="Check for Updates").click(check_for_updates) - gr.Button(value="Reload TTS").click(reload_tts) - with gr.Column(): - exec_inputs = exec_inputs + [ - gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size), - gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count), - gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate), - gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume), - ] - - for i in exec_inputs: - i.change( - fn=export_exec_settings, - inputs=exec_inputs - ) - - input_settings = [ - text, - delimiter, - emotion, - prompt, - voice, - mic_audio, - voice_latents_chunks, - seed, - candidates, - num_autoregressive_samples, - diffusion_iterations, - temperature, - diffusion_sampler, - breathing_room, - cvvp_weight, - top_p, - diffusion_temperature, - length_penalty, - repetition_penalty, - cond_free_k, - experimental_checkboxes, - ] - - # YUCK - def run_generation( - text, - delimiter, - emotion, - prompt, - voice, - mic_audio, - voice_latents_chunks, - seed, - candidates, - num_autoregressive_samples, - diffusion_iterations, - temperature, - diffusion_sampler, - breathing_room, - cvvp_weight, - top_p, - diffusion_temperature, - length_penalty, - repetition_penalty, - cond_free_k, - experimental_checkboxes, - progress=gr.Progress(track_tqdm=True) - ): - try: - sample, outputs, stats = generate( - text, - delimiter, - emotion, - prompt, - voice, - mic_audio, - voice_latents_chunks, - seed, - candidates, - num_autoregressive_samples, - diffusion_iterations, - temperature, - diffusion_sampler, - breathing_room, - cvvp_weight, - top_p, - diffusion_temperature, - length_penalty, - repetition_penalty, - cond_free_k, - experimental_checkboxes, - progress - ) - except Exception as e: - message = str(e) - if message == "Kill signal detected": - reload_tts() - - raise gr.Error(message) - - - return ( - outputs[0], - gr.update(value=sample, visible=sample is not None), - gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True), - gr.update(visible=len(outputs) > 1), - gr.update(value=stats, visible=True), - ) - - refresh_voices.click(update_voices, - inputs=None, - outputs=[ - voice, - history_voices - ] - ) - - output_pick.click( - lambda x: x, - inputs=candidates_list, - outputs=output_audio, - ) - - submit.click( - lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)), - outputs=[source_sample, candidates_list, output_pick, generation_results], - ) - - submit_event = submit.click(run_generation, - inputs=input_settings, - outputs=[output_audio, source_sample, candidates_list, output_pick, generation_results], - ) - - - copy_button.click(import_generate_settings, - inputs=audio_in, # JSON elements cannot be used as inputs - outputs=input_settings - ) - - def reset_generation_settings(): - with open(f'./config/generate.json', 'w', encoding="utf-8") as f: - f.write(json.dumps({}, indent='\t') ) - return import_generate_settings() - - reset_generation_settings_button.click( - fn=reset_generation_settings, - inputs=None, - outputs=input_settings - ) - - def history_copy_settings( voice, file ): - settings = import_generate_settings( f"./results/{voice}/{file}" ) - return settings - - history_copy_settings_button.click(history_copy_settings, - inputs=[ - history_voices, - history_results_list, - ], - outputs=input_settings - ) - - if os.path.isfile('./config/generate.json'): - webui.load(import_generate_settings, inputs=None, outputs=input_settings) - - if args.check_for_updates: - webui.load(check_for_updates) - - stop.click(fn=cancel_generate, inputs=None, outputs=None, cancels=[submit_event]) - - - webui.queue(concurrency_count=args.concurrency_count) - - return webui \ No newline at end of file