From f38c479e9b0c2e3f29454cf9aad965673ecd5a23 Mon Sep 17 00:00:00 2001
From: mrq <barry.quiggles@protonmail.com>
Date: Sun, 5 Feb 2023 06:17:51 +0000
Subject: [PATCH] Added multi-line parsing
---
app.py | 74 +++++++++++++++++++++++++++++++++++--------------
tortoise/api.py | 3 +-
2 files changed, 54 insertions(+), 23 deletions(-)
diff --git a/app.py b/app.py
index 9cb4738..2fb4bf2 100755
--- a/app.py
+++ b/app.py
@@ -8,8 +8,9 @@ import time
from datetime import datetime
from tortoise.api import TextToSpeech
from tortoise.utils.audio import load_audio, load_voice, load_voices
+from tortoise.utils.text import split_and_recombine_text
-def generate(text, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, progress=gr.Progress()):
+def generate(text, delimiter, emotion, prompt, voice, mic_audio, preset, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, progress=gr.Progress()):
if voice != "microphone":
voices = [voice]
else:
@@ -58,38 +59,67 @@ def generate(text, emotion, prompt, voice, mic_audio, preset, seed, candidates,
'progress': progress,
}
- gen, additionals = tts.tts( text, **settings )
- seed = additionals[0]
-
- info = f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} preset / {num_autoregressive_samples} samples / {diffusion_iterations} iterations | Temperature: {temperature} | Diffusion Sampler: {diffusion_sampler} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n".encode('utf8')
- with open("results.log", "w") as f:
- f.write(info)
+ if delimiter == "\\n":
+ delimiter = "\n"
+ if delimiter != "" and delimiter in text:
+ texts = text.split(delimiter)
+ else:
+ texts = split_and_recombine_text(text)
+
+
timestamp = int(time.time())
outdir = f"./results/{voice}/{timestamp}/"
-
+
os.makedirs(outdir, exist_ok=True)
+
+ # to-do: store audio to array to avoid having to re-read from disk when combining
+ # to-do: do not rejoin when not splitting lines
+
+ for line, cut_text in enumerate(texts):
+ print(f"[{str(line+1)}/{str(len(texts))}] Generating line: {cut_text}")
- with open(os.path.join(outdir, f'input.txt'), 'w') as f:
- f.write(f"{info}")
+ gen, additionals = tts.tts(cut_text, **settings )
+ seed = additionals[0]
+
+ if isinstance(gen, list):
+ for j, g in enumerate(gen):
+ os.makedirs(os.path.join(outdir, f'candidate_{j}'), exist_ok=True)
+ torchaudio.save(os.path.join(outdir, f'candidate_{j}/result_{line}.wav'), g.squeeze(0).cpu(), 24000)
+ else:
+ torchaudio.save(os.path.join(outdir, f'result_{line}.wav'), gen.squeeze(0).cpu(), 24000)
+
+ for candidate in range(candidates):
+ audio_clips = []
+ for line in range(len(texts)):
+ if isinstance(gen, list):
+ wav_file = os.path.join(outdir, f'candidate_{candidate}/result_{line}.wav')
+ else:
+ wav_file = os.path.join(outdir, f'result_{line}.wav')
- if isinstance(gen, list):
- for j, g in enumerate(gen):
- torchaudio.save(os.path.join(outdir, f'result_{j}.wav'), g.squeeze(0).cpu(), 24000)
-
- output_voice = gen[0]
- else:
- torchaudio.save(os.path.join(outdir, f'result.wav'), gen.squeeze(0).cpu(), 24000)
- output_voice = gen
+ audio_clips.append(load_audio(wav_file, 24000))
+ audio_clips = torch.cat(audio_clips, dim=-1)
+ torchaudio.save(os.path.join(outdir, f'combined_{candidate}.wav'), audio_clips, 24000)
+
+ info = f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} preset / {num_autoregressive_samples} samples / {diffusion_iterations} iterations | Temperature: {temperature} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
+
+ with open(os.path.join(outdir, f'input.txt'), 'w', encoding="utf-8") as f:
+ f.write(info)
- output_voice = (24000, output_voice.squeeze().cpu().numpy())
+ with open("results.log", "w", encoding="utf-8") as f:
+ f.write(info)
+ print(f"Saved to '{outdir}'")
+
+ output_voice = (24000, audio_clips.squeeze().cpu().numpy())
+
if sample_voice is not None:
sample_voice = (22050, sample_voice.squeeze().cpu().numpy())
-
+
+ audio_clips = []
return (
sample_voice,
- output_voice,
+ output_voice,
seed
)
@@ -112,6 +142,7 @@ def main():
with gr.Row():
with gr.Column():
text = gr.Textbox(lines=4, label="Prompt")
+ delimiter = gr.Textbox(lines=1, label="Multi-Line Delimiter", placeholder="\\n")
emotion = gr.Radio(
["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
@@ -179,6 +210,7 @@ def main():
submit_event = submit.click(generate,
inputs=[
text,
+ delimiter,
emotion,
prompt,
voice,
diff --git a/tortoise/api.py b/tortoise/api.py
index 643864c..a8bf54f 100755
--- a/tortoise/api.py
+++ b/tortoise/api.py
@@ -6,7 +6,7 @@ from urllib import request
if 'TORTOISE_MODELS_DIR' not in os.environ:
os.environ['TORTOISE_MODELS_DIR'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/tortoise/')
-
+
if 'TRANSFORMERS_CACHE' not in os.environ:
os.environ['TRANSFORMERS_CACHE'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/transformers/')
@@ -170,7 +170,6 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_la
noise = torch.randn(output_shape, device=latents.device) * temperature
mel = None
- print(f"Sampler: {sampler}")
if sampler == "P":
mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},