removed junk 2

app.py

@@ -39,7 +39,6 @@ iface = gr.Interface(
             value="L'Èlia i l'Alí a l'aula.  L'oli i l'ou.  Lulú olorava la lila.",
         ),
         gr.Dropdown(label="dialect", choices="")
-
     ],
     outputs=[
         gr.Markdown(label="Fonemes")

models/bsc/best_model.pth

@@ -1,3 +0,0 @@
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-size 1038659133

models/bsc/config.json

@@ -1,262 +0,0 @@
-{
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-    "project_name": null,
-    "run_description": "\ud83d\udc38Coqui trainer run.",
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-        ],
-        [
-            "Preguntin-se si aix\u00f2 era necessari."
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-        [
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-        [
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models/bsc/speaker_map.json

@@ -1,10 +0,0 @@
-{
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models/bsc/speakers.pth

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models/collectivat/catotron-ona-TTS-API-entry.json

@@ -1,10 +0,0 @@
-{
-    "voice": "ona-fast-hifigan",
-    "lang": "ca",
-    "model_type": "coqui",
-    "tts_config_path": "fast-speech_config.json",
-    "tts_model_path": "fast-speech_best_model.pth",
-    "vocoder_config_path": "ljspeech--hifigan_v2_config.json",
-    "vocoder_model_path": "ljspeech--hifigan_v2_model_file.pth",
-    "load": true
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models/collectivat/fast-speech_best_model.pth

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models/collectivat/fast-speech_config.json

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-{
-    "output_path": "/home/twbgmy/play/TTS-play/TTS/recipes/catotron",
-    "logger_uri": null,
-    "run_name": "fast_pitch_ljspeech",
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-    "run_description": "\ud83d\udc38Coqui trainer run.",
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models/collectivat/ljspeech--hifigan_v2_config.json

@@ -1,158 +0,0 @@
-{
-    "run_name": "hifigan",
-    "run_description": "universal hifigan trained on LibriTTS with no spectrogram normalization and using log() for scaling instead of log10()",
-
-
-    // AUDIO PARAMETERS
-    "audio":{
-        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
-        "win_length": 1024,      // stft window length in ms.
-        "hop_length": 256,       // stft window hop-lengh in ms.
-        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
-        // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "log_func": "np.log",
-
-        // Silence trimming
-        "do_trim_silence": false,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
-
-        // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
-        "spec_gain": 1.0,         // scaler value appplied after log transform of spectrogram.
-
-        // Normalization parameters
-        "signal_norm": false,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
-        "min_level_db": -100,   // lower bound for normalization
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
-    },
-
-    // DISTRIBUTED TRAINING
-    "distributed":{
-        "backend": "nccl",
-        "url": "tcp:\/\/localhost:54324"
-    },
-
-    // MODEL PARAMETERS
-    "use_pqmf": false,
-
-    // LOSS PARAMETERS
-    "use_stft_loss": false,
-    "use_subband_stft_loss": false,
-    "use_mse_gan_loss": true,
-    "use_hinge_gan_loss": false,
-    "use_feat_match_loss": true,  // use only with melgan discriminators
-    "use_l1_spec_loss": true,
-
-    // loss weights
-    "stft_loss_weight": 0,
-    "subband_stft_loss_weight": 0,
-    "mse_G_loss_weight": 1,
-    "hinge_G_loss_weight": 0,
-    "feat_match_loss_weight": 10,
-    "l1_spec_loss_weight": 45,
-
-    // multiscale stft loss parameters
-    // "stft_loss_params": {
-    //     "n_ffts": [1024, 2048, 512],
-    //     "hop_lengths": [120, 240, 50],
-    //     "win_lengths": [600, 1200, 240]
-    // },
-
-    "l1_spec_loss_params": {
-        "use_mel": true,
-        "sample_rate": 16000,
-        "n_fft": 1024,
-        "hop_length": 256,
-        "win_length": 1024,
-        "n_mels": 80,
-        "mel_fmin": 0.0,
-        "mel_fmax": null
-    },
-
-    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
-
-    // DISCRIMINATOR
-    "discriminator_model": "hifigan_discriminator",
-    //"discriminator_model_params":{
-    //    "peroids": [2, 3, 5, 7, 11],
-    //    "base_channels": 16,
-    //    "max_channels":512,
-    //    "downsample_factors":[4, 4, 4]
-    //},
-    "steps_to_start_discriminator": 0,      // steps required to start GAN trainining.1
-
-    // GENERATOR
-    "generator_model": "hifigan_generator",
-    "generator_model_params": {
-        "resblock_type": "1",
-        "upsample_factors": [8,8,2,2],
-        "upsample_kernel_sizes": [16,16,4,4],
-        "upsample_initial_channel": 128,
-        "resblock_kernel_sizes": [3,7,11],
-        "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]]
-    },
-
-    // DATASET
-    "data_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/vo_voice_quality_transformation/",
-    "feature_path": null,
-    // "feature_path": "/home/erogol/gdrive/Datasets/non-binary-voice-files/tacotron-DCA/",
-    "seq_len": 8192,
-    "pad_short": 2000,
-    "conv_pad": 0,
-    "use_noise_augment": false,
-    "use_cache": true,
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 16,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "epochs": 10000,                // total number of epochs to train.
-    "wd": 0.0,                // Weight decay weight.
-    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
-    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
-    // "lr_scheduler_gen": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
-    // "lr_scheduler_gen_params": {
-    //    "gamma": 0.999,
-        // "last_epoch": -1
-    // },
-    // "lr_scheduler_disc": "ExponentialLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
-    // "lr_scheduler_disc_params": {
-    	//   "gamma": 0.999,
-        // "last_epoch": -1
-    // },
-    "lr_gen": 0.00001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 0.00001,
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints loss values for each step in eval run.
-    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
-    // DATA LOADING
-    "num_loader_workers": 8,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "eval_split_size": 10,
-
-    // PATHS
-    "output_path": "/home/erogol/gdrive/Trainings/sam/"
-}
-
-

models/collectivat/ljspeech--hifigan_v2_model_file.pth

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-oid sha256:4047e93886faa1aba11948efa71f59dcb0ec9117e286660e59b91892ef98d129
-size 3794153

models/mms/G_100000.pth

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-version https://git-lfs.github.com/spec/v1
-oid sha256:0382edd70333f8ddc663177e672c8a66312e1b30f7929a8f9d458ef66f6b5349
-size 436622793

models/mms/config.json

@@ -1,87 +0,0 @@
-{
-    "train": {
-        "log_interval": 200,
-        "eval_interval": 1000,
-        "seed": 1234,
-        "epochs": 20000,
-        "learning_rate": 0.0002,
-        "betas": [
-            0.8,
-            0.99
-        ],
-        "eps": 1e-09,
-        "batch_size": 64,
-        "fp16_run": true,
-        "lr_decay": 0.999875,
-        "segment_size": 8192,
-        "init_lr_ratio": 1,
-        "warmup_epochs": 0,
-        "c_mel": 45,
-        "c_kl": 1.0
-    },
-    "data": {
-        "training_files": "train.ltr",
-        "validation_files": "dev.ltr",
-        "text_cleaners": [
-            "transliteration_cleaners"
-        ],
-        "max_wav_value": 32768.0,
-        "sampling_rate": 16000,
-        "filter_length": 1024,
-        "hop_length": 256,
-        "win_length": 1024,
-        "n_mel_channels": 80,
-        "mel_fmin": 0.0,
-        "mel_fmax": null,
-        "add_blank": true,
-        "n_speakers": 0,
-        "cleaned_text": true
-    },
-    "model": {
-        "inter_channels": 192,
-        "hidden_channels": 192,
-        "filter_channels": 768,
-        "n_heads": 2,
-        "n_layers": 6,
-        "kernel_size": 3,
-        "p_dropout": 0.1,
-        "resblock": "1",
-        "resblock_kernel_sizes": [
-            3,
-            7,
-            11
-        ],
-        "resblock_dilation_sizes": [
-            [
-                1,
-                3,
-                5
-            ],
-            [
-                1,
-                3,
-                5
-            ],
-            [
-                1,
-                3,
-                5
-            ]
-        ],
-        "upsample_rates": [
-            8,
-            8,
-            2,
-            2
-        ],
-        "upsample_initial_channel": 512,
-        "upsample_kernel_sizes": [
-            16,
-            16,
-            4,
-            4
-        ],
-        "n_layers_q": 3,
-        "use_spectral_norm": false
-    }
-}

models/mms/vocab.txt

@@ -1,39 +0,0 @@
-z
-f
-i
-g
-m
-o
-r
-è
-h
-l
-v
-à
-u
-d
-ú
-ç
-p
-s
-'
-é
-_
--
-e
-a
-—
-x
-ü
-q
-t
-b
-í
-ó
-ï
-ò
- 
-c
-j
-n
-y

models/piper/MODEL_CARD

@@ -1,15 +0,0 @@
-# Model card for upc_ona (x-low)
-
-* Language: ca (Catalan)
-* Speakers: 1
-* Quality: x-low
-* Samplerate: 16,000Hz
-
-## Dataset
-
-* URL: https://collectivat.cat/asr#upc-festcat-tts-corpora
-* License: CC BY-SA 3.0 ES
-
-## Training
-
-Trained from scratch.

models/piper/ca-upc_ona-x-low.onnx

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-size 20628813

models/piper/ca-upc_ona-x-low.onnx.json

@@ -1,409 +0,0 @@
-{
-    "audio": {
-        "sample_rate": 16000
-    },
-    "espeak": {
-        "voice": "ca"
-    },
-    "inference": {
-        "noise_scale": 0.667,
-        "length_scale": 1,
-        "noise_w": 0.8
-    },
-    "phoneme_map": {},
-    "phoneme_id_map": {
-        "_": [
-            0
-        ],
-        "^": [
-            1
-        ],
-        "$": [
-            2
-        ],
-        " ": [
-            3
-        ],
-        "!": [
-            4
-        ],
-        "'": [
-            5
-        ],
-        "(": [
-            6
-        ],
-        ")": [
-            7
-        ],
-        ",": [
-            8
-        ],
-        "-": [
-            9
-        ],
-        ".": [
-            10
-        ],
-        ":": [
-            11
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-        ";": [
-            12
-        ],
-        "?": [
-            13
-        ],
-        "a": [
-            14
-        ],
-        "b": [
-            15
-        ],
-        "c": [
-            16
-        ],
-        "d": [
-            17
-        ],
-        "e": [
-            18
-        ],
-        "f": [
-            19
-        ],
-        "h": [
-            20
-        ],
-        "i": [
-            21
-        ],
-        "j": [
-            22
-        ],
-        "k": [
-            23
-        ],
-        "l": [
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-        ],
-        "m": [
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-        ],
-        "n": [
-            26
-        ],
-        "o": [
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-        ],
-        "p": [
-            28
-        ],
-        "q": [
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-        ],
-        "r": [
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-        ],
-        "s": [
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-        ],
-        "t": [
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-        ],
-        "u": [
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-        ],
-        "v": [
-            34
-        ],
-        "w": [
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-        ],
-        "x": [
-            36
-        ],
-        "y": [
-            37
-        ],
-        "z": [
-            38
-        ],
-        "æ": [
-            39
-        ],
-        "ç": [
-            40
-        ],
-        "ð": [
-            41
-        ],
-        "ø": [
-            42
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-        "ħ": [
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-        ],
-        "ŋ": [
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-        "œ": [
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-        "ǁ": [
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-        "ǂ": [
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-        "θ": [
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-        "χ": [
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-        "ᵻ": [
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-        "ⱱ": [
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-        ]
-    },
-    "num_symbols": 130,
-    "num_speakers": 1,
-    "speaker_id_map": {}
-}

requirements.txt

@@ -1,2 +1,2 @@
 gradio
-espeak-phonemizer>=1.1.0,<2 
+espeak-phonemizer>=1.1.0,<2