Delete multi-speaker.py

multi-speaker.py

@@ -1,185 +0,0 @@
-import os
-# os.environ["CUDA_VISIBLE_DEVICES"] = "7"
-
-
-from trainer import Trainer, TrainerArgs
-
-from TTS.tts.configs.shared_configs import BaseDatasetConfig , CharactersConfig
-from TTS.config.shared_configs import BaseAudioConfig
-from TTS.tts.configs.vits_config import VitsConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.models.vits import Vits, VitsAudioConfig, VitsArgs
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.utils.audio import AudioProcessor
-from TTS.tts.utils.speakers import SpeakerManager
-
-#import wandb
- # Start a wandb run with `sync_tensorboard=True`
-#if wandb.run is None:
-    #wandb.init(project="persian-tts-vits-grapheme-cv15-fa-male-native-multispeaker-RERUN", group="GPUx8 accel mixed bf16 128x32", sync_tensorboard=True)
-
-# output_path = os.path.dirname(os.path.abspath(__file__))
-# output_path = output_path + '/notebook_files/runs'
-# output_path = wandb.run.dir  ### PROBABLY better for notebook
-output_path = "runs"
-
-# print("output path is:")
-# print(output_path)
-
-cache_path = "cache"
-
-
-
-# def mozilla(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
-#     """Normalizes Mozilla meta data files to TTS format"""
-#     txt_file = os.path.join(root_path, meta_file)
-#     items = []
-#     # speaker_name = "mozilla"
-#     with open(txt_file, "r", encoding="utf-8") as ttf:
-#         for line in ttf:
-#             cols = line.split("|")
-#             wav_file = cols[1].strip()
-#             text = cols[0].strip()
-#             speaker_name = cols[2].strip()
-#             wav_file = os.path.join(root_path, "wavs", wav_file)
-#             items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
-#     return items
-
-
-
-dataset_config = BaseDatasetConfig(
-    formatter='common_voice', meta_file_train='validated.tsv', path="/home/bargh1/TTS/datasets"
-)
-
-
-
-
-character_config=CharactersConfig(
-  characters='ءابتثجحخدذرزسشصضطظعغفقلمنهويِپچژکگیآأؤإئًَُّ',
-#   characters="!¡'(),-.:;¿?ABCDEFGHIJKLMNOPRSTUVWXYZabcdefghijklmnopqrstuvwxyzáçèéêëìíîïñòóôöùúûü«°±µ»$%&‘’‚“`”„",
-  punctuations='!(),-.:;? ̠،؛؟‌<>٫',
-  phonemes='ˈˌːˑpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟaegiouwyɪʊ̩æɑɔəɚɛɝɨ̃ʉʌʍ0123456789"#$%*+/=ABCDEFGHIJKLMNOPRSTUVWXYZ[]^_{}۱۲۳۴۵۶۷۸۹۰',
-  pad="<PAD>",
-  eos="<EOS>",
-  bos="<BOS>",
-  blank="<BLNK>",
-  characters_class="TTS.tts.models.vits.VitsCharacters",
-  )
-
-# From the coqui multilinguL recipes, will try later
-vitsArgs = VitsArgs(
-    # use_language_embedding=True,
-    # embedded_language_dim=1,
-    use_speaker_embedding=True,
-    use_sdp=False,
-)
-
-audio_config = BaseAudioConfig(
-     sample_rate=22050,
-     do_trim_silence=True,
-     min_level_db=-1,
-    # do_sound_norm=True,
-     signal_norm=True,
-     clip_norm=True,
-     symmetric_norm=True,
-     max_norm = 0.9,
-     resample=True,
-     win_length=1024,
-     hop_length=256,
-     num_mels=80,
-     mel_fmin=0,
-     mel_fmax=None
- )
-
-vits_audio_config = VitsAudioConfig(
-    sample_rate=22050,
-#    do_sound_norm=True,
-    win_length=1024,
-    hop_length=256,
-    num_mels=80,
-    # do_trim_silence=True, #from hugging
-    mel_fmin=0,
-    mel_fmax=None
-)
-config = VitsConfig(
-    model_args=vitsArgs,
-    audio=vits_audio_config, #from huggingface
-    run_name="persian-tts-vits-grapheme-cv15-multispeaker-RERUN",
-    use_speaker_embedding=True, ## For MULTI SPEAKER
-    batch_size=8,
-    batch_group_size=16,
-    eval_batch_size=4,
-    num_loader_workers=16,
-    num_eval_loader_workers=8,
-    run_eval=True,
-    run_eval_steps = 1000,
-    print_eval=True,
-    test_delay_epochs=-1,
-    epochs=1000,
-    save_step=1000,
-    text_cleaner="basic_cleaners", #from MH
-    use_phonemes=False,
-    # phonemizer='persian_mh', #from TTS github
-    # phoneme_language="fa",
-    characters=character_config, #test without as well
-    phoneme_cache_path=os.path.join(cache_path, "phoneme_cache_grapheme_azure-2"),
-    compute_input_seq_cache=True,
-    print_step=25,
-    mixed_precision=False, #from TTS - True causes error "Expected reduction dim"
-    test_sentences=[
-        ["زین همرهان سست عناصر، دلم گرفت."],
-        ["بیا تا گل برافشانیم و می در ساغر اندازیم."],
-        ["بنی آدم اعضای یک پیکرند, که در آفرینش ز یک گوهرند."],
-        ["سهام زندگی به 10 درصد و سهام بیتکوین گوگل به 33 درصد افزایش یافت."],
-        ["من بودم و آبجی فوتینا، و حالا رپتی پتینا. این شعر یکی از اشعار معروف رو حوضی است که در کوچه بازار تهران زمزمه می شده است." ],
-        ["یه دو دقه هم به حرفم گوش کن، نگو نگوشیدم و نحرفیدی."],
-        [ "داستان با توصیف طوفان‌های شدید آغاز می‌شود؛ طوفان‌هایی که مزرعه‌ها را از بین می‌برد و محصولات را زیر شن دفن می‌کند؛ محصولاتی که زندگی افراد بسیاری به آن وابسته است."]
-    ],
-    output_path=output_path,
-    datasets=[dataset_config]
-)
-
-# INITIALIZE THE AUDIO PROCESSOR
-# Audio processor is used for feature extraction and audio I/O.
-# It mainly serves to the dataloader and the training loggers.
-ap = AudioProcessor.init_from_config(config)
-
-# INITIALIZE THE TOKENIZER
-# Tokenizer is used to convert text to sequences of token IDs.
-# config is updated with the default characters if not defined in the config.
-tokenizer, config = TTSTokenizer.init_from_config(config)
-
-# LOAD DATA SAMPLES
-# Each sample is a list of ```[text, audio_file_path, speaker_name]```
-# You can define your custom sample loader returning the list of samples.
-# Or define your custom formatter and pass it to the `load_tts_samples`.
-# Check `TTS.tts.datasets.load_tts_samples` for more details.
-train_samples, eval_samples = load_tts_samples(
-    dataset_config,
-    eval_split=True,
-    eval_split_max_size=config.eval_split_max_size,
-    eval_split_size=config.eval_split_size,
-)
-
-# init speaker manager for multi-speaker training
-# it maps speaker-id to speaker-name in the model and data-loader
-speaker_manager = SpeakerManager()
-speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
-config.num_speakers = speaker_manager.num_speakers
-
-
-
-# init model
-model = Vits(config, ap, tokenizer, speaker_manager=speaker_manager)
-
-# init the trainer and 🚀
-
-trainer = Trainer(
-    TrainerArgs(use_accelerate=True),
-    config,
-    output_path,
-    model=model,
-    train_samples=train_samples,
-    eval_samples=eval_samples,
-)
-trainer.fit()