added tts streaming example

setup.py

@@ -4,7 +4,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 setuptools.setup(
-    name="TorToiSe",
+    name="tortoise-tts",
     packages=setuptools.find_packages(),
     version="3.0.0",
     author="James Betker",

tortoise/tts_stream.py

@@ -0,0 +1,85 @@
+import argparse
+import os
+from time import time
+
+import torch
+import torchaudio
+
+from api_fast import TextToSpeech, MODELS_DIR
+from utils.audio import load_audio, load_voices
+from utils.text import split_and_recombine_text
+import sounddevice as sd
+import queue
+import threading
+def play_audio(audio_queue):
+    while True:
+        chunk = audio_queue.get()
+        if chunk is None:
+            break
+        sd.play(chunk.cpu().numpy(), samplerate=24000)
+        sd.wait()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='lj')
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
+    parser.add_argument('--output_name', type=str, help='How to name the output file', default='combined.wav')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
+    parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
+                                                      'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
+    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=False)
+    parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True)
+    parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True)
+
+
+    args = parser.parse_args()
+    if torch.backends.mps.is_available():
+        args.use_deepspeed = False
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half)
+
+    outpath = args.output_path
+    outname = args.output_name
+    selected_voices = args.voice.split(',')
+    regenerate = args.regenerate
+    if regenerate is not None:
+        regenerate = [int(e) for e in regenerate.split(',')]
+
+    # Process text
+    with open(args.textfile, 'r', encoding='utf-8') as f:
+        text = ' '.join([l for l in f.readlines()])
+    if '|' in text:
+        print("Found the '|' character in your text, which I will use as a cue for where to split it up. If this was not"
+              "your intent, please remove all '|' characters from the input.")
+        texts = text.split('|')
+    else:
+        texts = split_and_recombine_text(text)
+    audio_queue = queue.Queue()
+    playback_thread = threading.Thread(target=play_audio, args=(audio_queue,))
+    playback_thread.start()
+    
+    seed = int(time()) if args.seed is None else args.seed
+    for selected_voice in selected_voices:
+        voice_outpath = os.path.join(outpath, selected_voice)
+        os.makedirs(voice_outpath, exist_ok=True)
+
+        if '&' in selected_voice:
+            voice_sel = selected_voice.split('&')
+        else:
+            voice_sel = [selected_voice]
+
+        voice_samples, conditioning_latents = load_voices(voice_sel)
+        all_parts = []
+        for j, text in enumerate(texts):
+            if regenerate is not None and j not in regenerate:
+                all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
+                continue
+            start_time = time()
+            audio_generator = tts.tts_stream(text, voice_samples=voice_samples, use_deterministic_seed=seed)
+            for wav_chunk in audio_generator:
+                audio_queue.put(wav_chunk)
+    audio_queue.put(None)
+    playback_thread.join()