spantrack

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",
-    "pillow",
+    "numpy", # --index-url https://download.pytorch.org/whl/cu132
+    "pillow",  # --index-url https://download.pytorch.org/whl/cu132
     "pydub",
     "soundfile",
-    "tqdm",
-    "audioop-lts; python_version >= '3.13'",
+    "torch", # --index-url https://download.pytorch.org/whl/cu132
+    "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]

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/__main__.py

@@ -33,7 +33,8 @@ from argparse import ArgumentParser
 from dotenv import load_dotenv
 
 # Local imports
-from . import tts_aedocw,  tts_generic, tts_kokoro
+from . import tts_aedocw,  tts_generic
+#from . import tts_kokoro
 from . import log, PathLike
 from .utils import check_env
 
@@ -54,7 +55,7 @@ BACKENDS = {
     "epub2tts-chatterbox": tts_aedocw.AedocwChatterbox,
     "epub2tts-kokoro": tts_aedocw.AedocwKokoro,
     "generic-epub2tts": tts_aedocw.Epub2TTS,
-    "kokoro": tts_kokoro.KokoroBackend,
+    #"kokoro": tts_kokoro.KokoroBackend,
 }
 
 

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)