Add application file

app.py

@@ -0,0 +1,201 @@
+import os
+import torch
+import gradio as gr
+import torchaudio
+import time
+from datetime import datetime
+from tortoise.api import TextToSpeech
+from tortoise.utils.text import split_and_recombine_text
+from tortoise.utils.audio import load_audio, load_voice, load_voices
+
+VOICE_OPTIONS = [
+    "angie",
+    "cond_latent_example",
+    "deniro",
+    "freeman",
+    "halle",
+    "lj",
+    "myself",
+    "pat2",
+    "snakes",
+    "tom",
+    "train_daws",
+    "train_dreams",
+    "train_grace",
+    "train_lescault",
+    "weaver",
+    "applejack",
+    "daniel",
+    "emma",
+    "geralt",
+    "jlaw",
+    "mol",
+    "pat",
+    "rainbow",
+    "tim_reynolds",
+    "train_atkins",
+    "train_dotrice",
+    "train_empire",
+    "train_kennard",
+    "train_mouse",
+    "william",
+    "random",  # special option for random voice
+    "disabled",  # special option for disabled voice
+]
+
+
+def inference(
+    text,
+    script,
+    name,
+    voice,
+    voice_b,
+    voice_c,
+    preset,
+    seed,
+    regenerate,
+    split_by_newline,
+):
+    if regenerate.strip() == "":
+        regenerate = None
+
+    if name.strip() == "":
+        raise gr.Error("No name provided")
+
+    if text is None or text.strip() == "":
+        with open(script.name) as f:
+            text = f.read()
+        if text.strip() == "":
+            raise gr.Error("Please provide either text or script file with content.")
+
+    if split_by_newline == "Yes":
+        texts = list(filter(lambda x: x.strip() != "", text.split("\n")))
+    else:
+        texts = split_and_recombine_text(text)
+
+    os.makedirs(os.path.join("longform", name), exist_ok=True)
+
+    if regenerate is not None:
+        regenerate = list(map(int, regenerate.split()))
+
+    voices = [voice]
+    if voice_b != "disabled":
+        voices.append(voice_b)
+    if voice_c != "disabled":
+        voices.append(voice_c)
+
+    if len(voices) == 1:
+        voice_samples, conditioning_latents = load_voice(voice)
+    else:
+        voice_samples, conditioning_latents = load_voices(voices)
+
+    start_time = time.time()
+
+    all_parts = []
+    for j, text in enumerate(texts):
+        if regenerate is not None and j + 1 not in regenerate:
+            all_parts.append(
+                load_audio(os.path.join("longform", name, f"{j+1}.wav"), 24000)
+            )
+            continue
+        gen = tts.tts_with_preset(
+            text,
+            voice_samples=voice_samples,
+            conditioning_latents=conditioning_latents,
+            preset=preset,
+            k=1,
+            use_deterministic_seed=seed,
+        )
+
+        gen = gen.squeeze(0).cpu()
+        torchaudio.save(os.path.join("longform", name, f"{j+1}.wav"), gen, 24000)
+
+        all_parts.append(gen)
+
+    full_audio = torch.cat(all_parts, dim=-1)
+
+    os.makedirs("outputs", exist_ok=True)
+    torchaudio.save(os.path.join("outputs", f"{name}.wav"), full_audio, 24000)
+
+    with open("Tortoise_TTS_Runs_Scripts.log", "a") as f:
+        f.write(
+            f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
+        )
+
+    output_texts = [f"({j+1}) {texts[j]}" for j in range(len(texts))]
+
+    return ((24000, full_audio.squeeze().cpu().numpy()), "\n".join(output_texts))
+
+
+def main():
+    text = gr.Textbox(
+        lines=4,
+        label="Text (Provide either text, or upload a newline separated text file below):",
+    )
+    script = gr.File(label="Upload a text file")
+    name = gr.Textbox(
+        lines=1, label="Name of the output file / folder to store intermediate results:"
+    )
+    preset = gr.Radio(
+        ["ultra_fast", "fast", "standard", "high_quality"],
+        value="fast",
+        label="Preset mode (determines quality with tradeoff over speed):",
+        type="value",
+    )
+    voice = gr.Dropdown(
+        VOICE_OPTIONS, value="angie", label="Select voice:", type="value"
+    )
+    voice_b = gr.Dropdown(
+        VOICE_OPTIONS,
+        value="disabled",
+        label="(Optional) Select second voice:",
+        type="value",
+    )
+    voice_c = gr.Dropdown(
+        VOICE_OPTIONS,
+        value="disabled",
+        label="(Optional) Select third voice:",
+        type="value",
+    )
+    seed = gr.Number(value=0, precision=0, label="Seed (for reproducibility):")
+    regenerate = gr.Textbox(
+        lines=1,
+        label="Comma-separated indices of clips to regenerate [starting from 1]",
+    )
+    split_by_newline = gr.Radio(
+        ["Yes", "No"],
+        label="Split by newline (If [No], it will automatically try to find relevant splits):",
+        type="value",
+        value="No",
+    )
+    output_audio = gr.Audio(label="Combined audio:")
+    output_text = gr.Textbox(label="Split texts with indices:", lines=10)
+
+    interface = gr.Interface(
+        fn=inference,
+        inputs=[
+            text,
+            script,
+            name,
+            voice,
+            voice_b,
+            voice_c,
+            preset,
+            seed,
+            regenerate,
+            split_by_newline,
+        ],
+        outputs=[output_audio, output_text],
+    )
+    interface.launch(share=True)
+
+
+if __name__ == "__main__":
+    tts = TextToSpeech(kv_cache=True, use_deepspeed=True, half=True)
+
+    with open("Tortoise_TTS_Runs_Scripts.log", "a") as f:
+        f.write(
+            f"\n\n-------------------------Tortoise TTS Scripts Logs, {datetime.now()}-------------------------\n"
+        )
+
+    main()