Remove obsolete package file tiny_tts/__init__.py

tiny_tts/__init__.py

@@ -1,90 +0,0 @@
-import os
-import torch
-import soundfile as sf
-from tiny_tts.text.english import normalize_text, grapheme_to_phoneme
-from tiny_tts.text import phonemes_to_ids
-from tiny_tts.nn import commons
-from tiny_tts.models.synthesizer import VoiceSynthesizer
-from tiny_tts.text.symbols import symbols
-from tiny_tts.utils.config import (
-    SAMPLING_RATE, SEGMENT_FRAMES, ADD_BLANK, SPEC_CHANNELS,
-    N_SPEAKERS, SPK2ID, MODEL_PARAMS,
-)
-from tiny_tts.infer import load_engine
-
-class TinyTTS:
-    def __init__(self, checkpoint_path=None, device=None):
-        if device is None:
-            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        else:
-            self.device = device
-            
-        if checkpoint_path is None:
-            # Look for default checkpoint in pacakage
-            pkg_dir = os.path.dirname(os.path.abspath(__file__))
-            default_ckpt = os.path.join(os.path.dirname(pkg_dir), "checkpoints", "G.pth")
-            # 2. Check HuggingFace Cache / Download
-            if not os.path.exists(default_ckpt):
-                try:
-                    from huggingface_hub import hf_hub_download
-                    print("Downloading/Loading checkpoint from Hugging Face Hub (backtracking/tiny-tts)...")
-                    default_ckpt = hf_hub_download(repo_id="backtracking/tiny-tts", filename="G.pth")
-                except ImportError:
-                    raise ImportError("huggingface_hub is required to auto-download the model. Run: pip install huggingface_hub")
-                except Exception as e:
-                    raise ValueError(f"Failed to download checkpoint from Hugging Face: {e}")
-
-            checkpoint_path = default_ckpt
-                
-        self.model = load_engine(checkpoint_path, self.device)
-
-    def speak(self, text, output_path="output.wav", speaker="MALE", speed=1.0):
-        """Synthesize text to speech and save to output_path."""
-        print(f"Synthesizing: {text}")
-
-        # Normalize text
-        normalized = normalize_text(text)
-
-        # Phonemize
-        phones, tones, word2ph = grapheme_to_phoneme(normalized)
-
-        # Convert to sequence
-        phone_ids, tone_ids, lang_ids = phonemes_to_ids(phones, tones, "EN")
-
-        # Add blanks
-        if ADD_BLANK:
-            phone_ids = commons.insert_blanks(phone_ids, 0)
-            tone_ids = commons.insert_blanks(tone_ids, 0)
-            lang_ids = commons.insert_blanks(lang_ids, 0)
-
-        x = torch.LongTensor(phone_ids).unsqueeze(0).to(self.device)
-        x_lengths = torch.LongTensor([len(phone_ids)]).to(self.device)
-        tone = torch.LongTensor(tone_ids).unsqueeze(0).to(self.device)
-        language = torch.LongTensor(lang_ids).unsqueeze(0).to(self.device)
-
-        # Speaker ID
-        if speaker not in SPK2ID:
-            print(f"Warning: Speaker '{speaker}' not found, using ID 0. Available: {list(SPK2ID.keys())}")
-            sid = torch.LongTensor([0]).to(self.device)
-        else:
-            sid = torch.LongTensor([SPK2ID[speaker]]).to(self.device)
-
-        # BERT features (disabled - using zero tensors)
-        bert = torch.zeros(1024, len(phone_ids)).to(self.device).unsqueeze(0)
-        ja_bert = torch.zeros(768, len(phone_ids)).to(self.device).unsqueeze(0)
-
-        # speed > 1.0 = faster speech, < 1.0 = slower speech
-        length_scale = 1.0 / speed
-
-        with torch.no_grad():
-            audio, *_ = self.model.infer(
-                x, x_lengths, sid, tone, language, bert, ja_bert,
-                noise_scale=0.667,
-                noise_scale_w=0.8,
-                length_scale=length_scale
-            )
-
-        audio_np = audio[0, 0].cpu().numpy()
-        sf.write(output_path, audio_np, SAMPLING_RATE)
-        print(f"Saved audio to {output_path}")
-        return audio_np