Upload multi-speaker.py

multi-speaker.py

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+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()