.

pyproject.toml

@@ -27,19 +27,22 @@ classifiers = [
 ]
 
 dependencies = [
-    "load-dotenv>=0.1.0",
+    "audioop-lts; python_version >= '3.13'",
     "beautifulsoup4",
     "ebooklib",
     "kokoro>=0.9.4",
+    "load-dotenv>=0.1.0",
     "lxml",
     "mutagen",
     "nltk",
-    "numpy",
+    "numpy", # --index-url https://download.pytorch.org/whl/cu132
-    "pillow",
+    "pillow",  # --index-url https://download.pytorch.org/whl/cu132
     "pydub",
     "soundfile",
-    "tqdm",
+    "torch", # --index-url https://download.pytorch.org/whl/cu132
-    "audioop-lts; python_version >= '3.13'",
+    "torchaudio", # --index-url https://download.pytorch.org/whl/cu132
+    "torchcodec", # PyTorch 2.9+ # --index-url https://download.pytorch.org/whl/cu132
+    "tqdm", # --index-url https://download.pytorch.org/whl/cu132
 ]
 
 [project.optional-dependencies]

src/epub_tts/__main__.py

@@ -4,7 +4,116 @@
 # See [LICENSE](./LICENSE) or [BSD-3-Clause-Clear](https://spdx.org/licenses/BSD-3-Clause-Clear.html)
 
 import sys
+<<<<<<< HEAD
+from pathlib import Path
+from argparse import ArgumentParser
+
+# Pip packages
+from dotenv import load_dotenv
+
+# Local imports
+from . import tts_aedocw,  tts_generic
+#from . import tts_kokoro
+from . import log, PathLike
+from .utils import check_env
+
+# Setup env
+load_dotenv()  # load environment variables from .env file if present
+
+# Globals
+WHICH = ["ffmpeg"] # Neded in $PATH
+BACKENDS = {
+    # keys are the backend names, values are the corresponding TTS classes
+    "default": tts_generic.GenericTTSBackend,
+    "edge": tts_aedocw.TTSEdge,
+    # aedocw-backed implementations — these expect the corresponding
+    # package "console scripts"/entrypoints to be available in the
+    # environment (or an explicit backend_cmd to be provided).
+    "epub2tts": tts_aedocw.AedocwEpub2TTS,
+    "epub2tts-edge": tts_aedocw.AedocwEpub2TTSEdge,
+    "epub2tts-chatterbox": tts_aedocw.AedocwChatterbox,
+    "epub2tts-kokoro": tts_aedocw.AedocwKokoro,
+    "generic-epub2tts": tts_aedocw.Epub2TTS,
+    #"kokoro": tts_kokoro.KokoroBackend,
+}
+
+
+def main(args=None):
+    p = ArgumentParser(description="Convert EPUB to audio using TTS")
+    p.add_argument(
+        "--version",
+        help="Show version and exit",
+        action="version",
+        version=f"%(prog)s {__import__('epub_tts').__version__}",
+    )
+    # Input options and processing
+    p.add_argument("-i", "--input", 
+                   nargs="+", required=True,
+                   help="Input EPUB file")
+    p.add_argument("-r", "--replace", action="append", nargs=2, 
+                   help="Replace text in the intermediate output. "
+                   "Specify pairs of old_text new_text. "
+                   "Can be used multiple times.")
+    p.add_argument("--check-env",action="store_true",
+                    help="Check the runtime environment and exit")
+    p.add_argument("-b", "--backend", default="default",
+                   choices=BACKENDS.keys(),
+                   help="Backend to use for TTS")
+
+    # Output options
+    p.add_argument("-o", "--output", 
+                   help="Output audio file", required=True)
+    p.add_argument("-c", "--cover", 
+                   help="Path to cover image to embed or use "
+                   "for output metadata")
+    
+    # Speech options
+    p.add_argument("-l", "--language", 
+                   help="Language to use for TTS (see your "
+                   "backend's documentation for available voices)")
+    p.add_argument("-v", "--voice", 
+                   help="Voice to use for TTS (see your backend's "
+                   "documentation for available voices)")
+    p.add_argument("--speed", type=float, default=1.0, 
+                   help="Playback speed multiplier (default: 1.0) "
+                   "(not all backends support this)")
+    p.add_argument("--short-pause", type=int, default=None, 
+                   help="Short pause duration in milliseconds between "
+                   "phrases or sentences (not all backends support this)")
+    p.add_argument("--long-pause", type=int, default=None, 
+                   help="Long pause duration in milliseconds between "
+                   "sections or paragraphs (not all backends support this)")
+    p.add_argument("--notitles", action="store_true",
+                   help="Do not read chapter titles")
+
+    # Parse
+    args = p.parse_args(args or sys.argv[1:])
+    setattr(args, 'replace_map', 
+            {old: new for old, new in args.replace} if args.replace else None)
+    log(args)
+
+    # Execute actions
+    if args.check_env:
+        check_env()
+           
+    engine = BACKENDS[args.backend](**vars(args))
+    if len(args.input) > 1 and not output_dest.is_dir():
+        log(f"Output must be a directory when multiple input files are provided", level="error")
+        sys.exit(1)
+    for file in args.input:
+        log(file)
+        if not Path(file).exists():
+            log(f"Input file {file} does not exist", level="error")
+            sys.exit(1)
+        output_dest = Path(args.output)
+        engine.run(
+            file, output_dest, 
+            **vars(args),
+            )
+    
+=======
 from .cli import cli
+>>>>>>> dev
 
 
 if __name__ == "__main__":

src/epub_tts/spantrack.py

@@ -0,0 +1,22 @@
+import os
+import logging
+logger = logging.get_logger()
+os.environ["NLTK_DATA"] =  'C:\\Users\\renat\\AppData\\Roaming\\nltk_data'
+os.environ["HF_TOKEN"] = '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'
+from spantrack import cli
+root = 'D:\\DATA\\EBOOKS\\_SEM_DRM\\'
+globs = [
+"Night School*.epub",
+"The Midnight*.epub",
+"Past Tense*.epub",
+"Blue Moon*.epub",
+"The Sentinel",
+"Better Off Dead",
+"No Plan B",
+]
+for glob in globs:
+  f = root.glob(glob)
+  if len(f) > 1:
+    logger.error("Glob pattern found more than 1 file: %s", f)
+    continue
+  cli.main([f.resolve(), "--voice", "am_liam"])

src/epub_tts/tts_kokoro.py

@@ -9,12 +9,14 @@ No vendored repository needed, kokoro is a pure Python package that can be insta
 
 # stdlib modules
 import subprocess
+from pathlib import Path
 import os
 import sys
 # Automatically enable MPS fallback on Apple Silicon macOS
 if sys.platform == 'darwin':
     os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
 
+
 # pip installed packages
 import numpy as np
 import soundfile
@@ -203,3 +205,109 @@ class KokoroBackend(GenericTTSBackend):
                     os.remove(file)
                 segments.append(partname)
         return segments
+
+    # ***
+
+    # READ
+    def read_from_txt_to_wav(text_file:PathLike, 
+                             speaker:str="af_heart") -> Path:
+        if not isinstance(text_file,Path):
+            text_file = Path(text_file).resolve()
+        else:
+            text_file = text_file.resolve()
+
+        # Check if text file exists
+        if not text_file.exists():
+            print(f"Error: Text file '{text_file}' not found.")
+            return False
+
+        # Read the text from the file
+        with open(text_file, 'r', encoding='utf-8') as f:
+            text_contents = f.read()
+
+        # Generate output filename (replace .txt extension with .wav)
+        output_file = text_file.with_suffix('.wav')
+
+        # Check for CUDA GPU
+        if torch.cuda.is_available():
+            print('CUDA GPU available')
+            torch.set_default_device('cuda')
+
+        print(f"Generating audio for speaker '{speaker}' from '{text_file}'...")
+
+        # Create pipeline with language code (first character of speaker name)
+        pipeline = KPipeline(lang_code=speaker[0])
+
+        # Generate audio segments
+        audio_segments = []
+        for gs, ps, audio in pipeline(text_contents, voice=speaker, speed=1, split_pattern=r'\n\n\n'):
+            audio_segments.append(audio)
+
+        # Concatenate all audio segments
+        final_audio = np.concatenate(audio_segments)
+
+        # Write to wav file
+        soundfile.write(output_file, final_audio, 24000)
+
+        print(f"Audio saved to '{output_file}'")
+        return output_file
+
+
+
+    def get_speakers(self) -> list[str]:
+        """Return list of available speakers.
+        See https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md"""
+        speakers = [
+            # 🇺🇸 American English: 11F 9M
+            # Overall Grade 'A'
+            "af_heart", 
+            # Overall Grade 'C+'
+            "af_aoede","af_kore","af_sarah",
+            "am_fenrir","am_michael","am_puck",
+            # Other
+            "af_alloy",  "af_bella", "af_jessica",  "af_nicole", "af_nova", "af_river",  "af_sky", "am_adam", "am_echo", "am_eric",  "am_liam",  "am_onyx",  "am_santa", "bf_alice", 
+            # 🇬🇧 British English: 4F 4M
+            "bf_emma", "bf_isabella", "bf_lily", "bm_daniel", "bm_fable", "bm_george", "bm_lewis",
+            # 🇧🇷 Brazilian Portuguese: 1F 2M
+            "pf_dora",
+            "pm_alex",
+            "pm_santa"]
+            # Old list:
+            # ["af_heart", "af_joy", "af_sad", "af_angry", "af_fear", "af_surprise"]
+        return speakers
+    
+    def gen_speaker_samples(
+            self, 
+            samples: list =None,
+            output_path: PathLike=None,
+        ) -> list[str]:
+        """Generate sample speakers audio in output_path."""
+        result = self._build_command("gen_samples.py", *samples, output_path=output_path)
+        return [str(self._normalize_path(s)) for s in samples]
+    
+    def gen_speaker_samples():
+        
+
+        if torch.cuda.is_available():
+            print('CUDA GPU available')
+            torch.set_default_device('cuda')
+
+        for speaker in speakers:
+            file = speaker + "_sample.wav"
+            if os.path.exists(file):
+                print(f"Sample for {speaker} already exists.")
+                continue
+            else:
+                print(f"Creating {speaker}")
+            pipeline = KPipeline(lang_code=speaker[0])
+            sentence = f"Hello, this voice is {speaker[3:]}. The quick brown fox jumped over the lazy dog. The fish twisted and turned on the bent hook.  Press the pants and sew a button on the vest.  The swan dive was far short of perfect."
+            audio_segments = []
+            for gs, ps, audio in pipeline(
+                sentence, 
+                repo_id='hexgrad/Kokoro-82M',
+                voice=speaker, 
+                speed=1, 
+                split_pattern=r'\n\n\n'):
+                audio_segments.append(audio)
+            final_audio = np.concatenate(audio_segments)
+            soundfile.write(file, final_audio, 24000)