Upload 4 files

1/best_model.pth

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+oid sha256:36c51edb6b337fe3c2cc91dbf555d0d4785a6aa45c6207c7cc103adc618903b0
+size 5607926933

1/config.json

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+{
+    "output_path": "output",
+    "logger_uri": null,
+    "run_name": "run",
+    "project_name": null,
+    "run_description": "\ud83d\udc38Coqui trainer run.",
+    "print_step": 25,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "save_on_interrupt": true,
+    "log_model_step": null,
+    "save_step": 10000,
+    "save_n_checkpoints": 5,
+    "save_checkpoints": true,
+    "save_all_best": false,
+    "save_best_after": 10000,
+    "target_loss": null,
+    "print_eval": false,
+    "test_delay_epochs": 0,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": false,
+    "precision": "fp16",
+    "epochs": 1000,
+    "batch_size": 32,
+    "eval_batch_size": 16,
+    "grad_clip": 0.0,
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "radam",
+    "optimizer_params": null,
+    "lr_scheduler": null,
+    "lr_scheduler_params": {},
+    "use_grad_scaler": false,
+    "allow_tf32": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "xtts",
+    "num_loader_workers": 0,
+    "num_eval_loader_workers": 0,
+    "use_noise_augment": false,
+    "audio": {
+        "sample_rate": 22050,
+        "output_sample_rate": 24000
+    },
+    "use_phonemes": false,
+    "phonemizer": null,
+    "phoneme_language": null,
+    "compute_input_seq_cache": false,
+    "text_cleaner": null,
+    "enable_eos_bos_chars": false,
+    "test_sentences_file": "",
+    "phoneme_cache_path": null,
+    "characters": null,
+    "add_blank": false,
+    "batch_group_size": 0,
+    "loss_masking": null,
+    "min_audio_len": 1,
+    "max_audio_len": Infinity,
+    "min_text_len": 1,
+    "max_text_len": Infinity,
+    "compute_f0": false,
+    "compute_energy": false,
+    "compute_linear_spec": false,
+    "precompute_num_workers": 0,
+    "start_by_longest": false,
+    "shuffle": false,
+    "drop_last": false,
+    "datasets": [
+        {
+            "formatter": "",
+            "dataset_name": "",
+            "path": "",
+            "meta_file_train": "",
+            "ignored_speakers": null,
+            "language": "",
+            "phonemizer": "",
+            "meta_file_val": "",
+            "meta_file_attn_mask": ""
+        }
+    ],
+    "test_sentences": [],
+    "eval_split_max_size": null,
+    "eval_split_size": 0.01,
+    "use_speaker_weighted_sampler": false,
+    "speaker_weighted_sampler_alpha": 1.0,
+    "use_language_weighted_sampler": false,
+    "language_weighted_sampler_alpha": 1.0,
+    "use_length_weighted_sampler": false,
+    "length_weighted_sampler_alpha": 1.0,
+    "model_args": {
+        "gpt_batch_size": 1,
+        "enable_redaction": false,
+        "kv_cache": true,
+        "gpt_checkpoint": null,
+        "clvp_checkpoint": null,
+        "decoder_checkpoint": null,
+        "num_chars": 255,
+        "tokenizer_file": "",
+        "gpt_max_audio_tokens": 605,
+        "gpt_max_text_tokens": 402,
+        "gpt_max_prompt_tokens": 70,
+        "gpt_layers": 30,
+        "gpt_n_model_channels": 1024,
+        "gpt_n_heads": 16,
+        "gpt_number_text_tokens": 6681,
+        "gpt_start_text_token": null,
+        "gpt_stop_text_token": null,
+        "gpt_num_audio_tokens": 1026,
+        "gpt_start_audio_token": 1024,
+        "gpt_stop_audio_token": 1025,
+        "gpt_code_stride_len": 1024,
+        "gpt_use_masking_gt_prompt_approach": true,
+        "gpt_use_perceiver_resampler": true,
+        "input_sample_rate": 22050,
+        "output_sample_rate": 24000,
+        "output_hop_length": 256,
+        "decoder_input_dim": 1024,
+        "d_vector_dim": 512,
+        "cond_d_vector_in_each_upsampling_layer": true,
+        "duration_const": 102400
+    },
+    "model_dir": null,
+    "languages": [
+        "en",
+        "es",
+        "fr",
+        "de",
+        "it",
+        "pt",
+        "pl",
+        "tr",
+        "ru",
+        "nl",
+        "cs",
+        "ar",
+        "zh-cn",
+        "hu",
+        "ko",
+        "ja",
+        "hi"
+    ],
+    "temperature": 0.75,
+    "length_penalty": 1.0,
+    "repetition_penalty": 5.0,
+    "top_k": 50,
+    "top_p": 0.85,
+    "num_gpt_outputs": 1,
+    "gpt_cond_len": 30,
+    "gpt_cond_chunk_len": 4,
+    "max_ref_len": 30,
+    "sound_norm_refs": false
+}

1/xtts_demo.py

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+import argparse
+import os
+import sys
+import tempfile
+
+import gradio as gr
+import librosa.display
+import numpy as np
+
+import os
+import torch
+import torchaudio
+import traceback
+from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
+from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
+
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+
+def clear_gpu_cache():
+    # clear the GPU cache
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+XTTS_MODEL = None
+def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
+    global XTTS_MODEL
+    clear_gpu_cache()
+    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
+        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
+    config = XttsConfig()
+    config.load_json(xtts_config)
+    XTTS_MODEL = Xtts.init_from_config(config)
+    print("Loading XTTS model! ")
+    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
+    if torch.cuda.is_available():
+        XTTS_MODEL.cuda()
+
+    print("Model Loaded!")
+    return "Model Loaded!"
+
+def run_tts(lang, tts_text, speaker_audio_file):
+    if XTTS_MODEL is None or not speaker_audio_file:
+        return "You need to run the previous step to load the model !!", None, None
+
+    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
+    out = XTTS_MODEL.inference(
+        text=tts_text,
+        language=lang,
+        gpt_cond_latent=gpt_cond_latent,
+        speaker_embedding=speaker_embedding,
+        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
+        length_penalty=XTTS_MODEL.config.length_penalty,
+        repetition_penalty=XTTS_MODEL.config.repetition_penalty,
+        top_k=XTTS_MODEL.config.top_k,
+        top_p=XTTS_MODEL.config.top_p,
+    )
+
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
+        out_path = fp.name
+        torchaudio.save(out_path, out["wav"], 24000)
+
+    return "Speech generated !", out_path, speaker_audio_file
+
+
+
+
+# define a logger to redirect 
+class Logger:
+    def __init__(self, filename="log.out"):
+        self.log_file = filename
+        self.terminal = sys.stdout
+        self.log = open(self.log_file, "w")
+
+    def write(self, message):
+        self.terminal.write(message)
+        self.log.write(message)
+
+    def flush(self):
+        self.terminal.flush()
+        self.log.flush()
+
+    def isatty(self):
+        return False
+
+# redirect stdout and stderr to a file
+sys.stdout = Logger()
+sys.stderr = sys.stdout
+
+
+# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+import logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+
+def read_logs():
+    sys.stdout.flush()
+    with open(sys.stdout.log_file, "r") as f:
+        return f.read()
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(
+        description="""XTTS fine-tuning demo\n\n"""
+        """
+        Example runs:
+        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port 
+        """,
+        formatter_class=argparse.RawTextHelpFormatter,
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        help="Port to run the gradio demo. Default: 5003",
+        default=5003,
+    )
+    parser.add_argument(
+        "--out_path",
+        type=str,
+        help="Output path (where data and checkpoints will be saved) Default: /tmp/xtts_ft/",
+        default="/tmp/xtts_ft/",
+    )
+
+    parser.add_argument(
+        "--num_epochs",
+        type=int,
+        help="Number of epochs to train. Default: 10",
+        default=10,
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        help="Batch size. Default: 4",
+        default=4,
+    )
+    parser.add_argument(
+        "--grad_acumm",
+        type=int,
+        help="Grad accumulation steps. Default: 1",
+        default=1,
+    )
+    parser.add_argument(
+        "--max_audio_length",
+        type=int,
+        help="Max permitted audio size in seconds. Default: 11",
+        default=11,
+    )
+
+    args = parser.parse_args()
+
+    with gr.Blocks() as demo:
+        with gr.Tab("1 - Data processing"):
+            out_path = gr.Textbox(
+                label="Output path (where data and checkpoints will be saved):",
+                value=args.out_path,
+            )
+            # upload_file = gr.Audio(
+            #     sources="upload",
+            #     label="Select here the audio files that you want to use for XTTS trainining !",
+            #     type="filepath",
+            # )
+            upload_file = gr.File(
+                file_count="multiple",
+                label="Select here the audio files that you want to use for XTTS trainining (Supported formats: wav, mp3, and flac)",
+            )
+            lang = gr.Dropdown(
+                label="Dataset Language",
+                value="en",
+                choices=[
+                    "en",
+                    "es",
+                    "fr",
+                    "de",
+                    "it",
+                    "pt",
+                    "pl",
+                    "tr",
+                    "ru",
+                    "nl",
+                    "cs",
+                    "ar",
+                    "zh",
+                    "hu",
+                    "ko",
+                    "ja"
+                ],
+            )
+            progress_data = gr.Label(
+                label="Progress:"
+            )
+            logs = gr.Textbox(
+                label="Logs:",
+                interactive=False,
+            )
+            demo.load(read_logs, None, logs, every=1)
+
+            prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
+        
+            def preprocess_dataset(audio_path, language, out_path, progress=gr.Progress(track_tqdm=True)):
+                clear_gpu_cache()
+                out_path = os.path.join(out_path, "dataset")
+                os.makedirs(out_path, exist_ok=True)
+                if audio_path is None:
+                    return "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!", "", ""
+                else:
+                    try:
+                        train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
+                    except:
+                        traceback.print_exc()
+                        error = traceback.format_exc()
+                        return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""
+
+                clear_gpu_cache()
+
+                # if audio total len is less than 2 minutes raise an error
+                if audio_total_size < 120:
+                    message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
+                    print(message)
+                    return message, "", ""
+
+                print("Dataset Processed!")
+                return "Dataset Processed!", train_meta, eval_meta
+
+        with gr.Tab("2 - Fine-tuning XTTS Encoder"):
+            train_csv = gr.Textbox(
+                label="Train CSV:",
+            )
+            eval_csv = gr.Textbox(
+                label="Eval CSV:",
+            )
+            num_epochs =  gr.Slider(
+                label="Number of epochs:",
+                minimum=1,
+                maximum=100,
+                step=1,
+                value=args.num_epochs,
+            )
+            batch_size = gr.Slider(
+                label="Batch size:",
+                minimum=2,
+                maximum=512,
+                step=1,
+                value=args.batch_size,
+            )
+            grad_acumm = gr.Slider(
+                label="Grad accumulation steps:",
+                minimum=2,
+                maximum=128,
+                step=1,
+                value=args.grad_acumm,
+            )
+            max_audio_length = gr.Slider(
+                label="Max permitted audio size in seconds:",
+                minimum=2,
+                maximum=20,
+                step=1,
+                value=args.max_audio_length,
+            )
+            progress_train = gr.Label(
+                label="Progress:"
+            )
+            logs_tts_train = gr.Textbox(
+                label="Logs:",
+                interactive=False,
+            )
+            demo.load(read_logs, None, logs_tts_train, every=1)
+            train_btn = gr.Button(value="Step 2 - Run the training")
+
+            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+                clear_gpu_cache()
+                if not train_csv or not eval_csv:
+                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                try:
+                    # convert seconds to waveform frames
+                    max_audio_length = int(max_audio_length * 22050)
+                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
+                except:
+                    traceback.print_exc()
+                    error = traceback.format_exc()
+                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
+
+                # copy original files to avoid parameters changes issues
+                os.system(f"cp {config_path} {exp_path}")
+                os.system(f"cp {vocab_file} {exp_path}")
+
+                ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
+                print("Model training done!")
+                clear_gpu_cache()
+                return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
+
+        with gr.Tab("3 - Inference"):
+            with gr.Row():
+                with gr.Column() as col1:
+                    xtts_checkpoint = gr.Textbox(
+                        label="XTTS checkpoint path:",
+                        value="",
+                    )
+                    xtts_config = gr.Textbox(
+                        label="XTTS config path:",
+                        value="",
+                    )
+
+                    xtts_vocab = gr.Textbox(
+                        label="XTTS vocab path:",
+                        value="",
+                    )
+                    progress_load = gr.Label(
+                        label="Progress:"
+                    )
+                    load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")
+
+                with gr.Column() as col2:
+                    speaker_reference_audio = gr.Textbox(
+                        label="Speaker reference audio:",
+                        value="",
+                    )
+                    tts_language = gr.Dropdown(
+                        label="Language",
+                        value="en",
+                        choices=[
+                            "en",
+                            "es",
+                            "fr",
+                            "de",
+                            "it",
+                            "pt",
+                            "pl",
+                            "tr",
+                            "ru",
+                            "nl",
+                            "cs",
+                            "ar",
+                            "zh",
+                            "hu",
+                            "ko",
+                            "ja",
+                        ]
+                    )
+                    tts_text = gr.Textbox(
+                        label="Input Text.",
+                        value="This model sounds really good and above all, it's reasonably fast.",
+                    )
+                    tts_btn = gr.Button(value="Step 4 - Inference")
+
+                with gr.Column() as col3:
+                    progress_gen = gr.Label(
+                        label="Progress:"
+                    )
+                    tts_output_audio = gr.Audio(label="Generated Audio.")
+                    reference_audio = gr.Audio(label="Reference audio used.")
+
+            prompt_compute_btn.click(
+                fn=preprocess_dataset,
+                inputs=[
+                    upload_file,
+                    lang,
+                    out_path,
+                ],
+                outputs=[
+                    progress_data,
+                    train_csv,
+                    eval_csv,
+                ],
+            )
+
+
+            train_btn.click(
+                fn=train_model,
+                inputs=[
+                    lang,
+                    train_csv,
+                    eval_csv,
+                    num_epochs,
+                    batch_size,
+                    grad_acumm,
+                    out_path,
+                    max_audio_length,
+                ],
+                outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
+            )
+            
+            load_btn.click(
+                fn=load_model,
+                inputs=[
+                    xtts_checkpoint,
+                    xtts_config,
+                    xtts_vocab
+                ],
+                outputs=[progress_load],
+            )
+
+            tts_btn.click(
+                fn=run_tts,
+                inputs=[
+                    tts_language,
+                    tts_text,
+                    speaker_reference_audio,
+                ],
+                outputs=[progress_gen, tts_output_audio, reference_audio],
+            )
+
+    demo.launch(
+        share=True,
+        debug=False,
+        server_port=args.port,
+        server_name="0.0.0.0"
+    )