voice-cloning-collab/demo_cli.py

import argparse
from ctypes import alignment
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from pathlib import Path
import spacy
import time


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("--run_id", type=str, default="default", help= \
    "Name for this model. By default, training outputs will be stored to saved_models/<run_id>/. If a model state "
    "from the same run ID was previously saved, the training will restart from there. Pass -f to overwrite saved "
    "states and restart from scratch.")
    parser.add_argument("-m", "--models_dir", type=Path, default="saved_models",
                        help="Directory containing all saved models")
    parser.add_argument("--weight", type=float, default=1,
                        help="weight of input audio for voice filter")
    parser.add_argument("--griffin_lim",
                        action="store_true",
                        help="if True, use vocoder, else use griffin-lim")
    parser.add_argument("--cpu", action="store_true", help=\
        "If True, processing is done on CPU, even when a GPU is available.")
    parser.add_argument("--no_sound", action="store_true", help=\
        "If True, audio won't be played.")
    parser.add_argument("--seed", type=int, default=None, help=\
        "Optional random number seed value to make toolbox deterministic.")
    args = parser.parse_args()
    arg_dict = vars(args)
    # print_args(args, parser)

    # Hide GPUs from Pytorch to force CPU processing
    if arg_dict.pop("cpu"):
        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

    print("Running a test of your configuration...\n")

    import numpy as np
    import soundfile as sf
    import torch

    import encoder.inference
    import encoder.params_data 
    from synthesizer.inference import Synthesizer
    from synthesizer.utils.cleaners import add_breaks, english_cleaners
    from vocoder import inference as vocoder
    from vocoder.display import save_attention, save_spectrogram, save_stop_tokens
    from utils.argutils import print_args
    from utils.default_models import ensure_default_models
    from speed_changer.fixSpeed import *
    if torch.cuda.is_available():
        device_id = torch.cuda.current_device()
        gpu_properties = torch.cuda.get_device_properties(device_id)
        ## Print some environment information (for debugging purposes)
        print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
            "%.1fGb total memory.\n" %
            (torch.cuda.device_count(),
            device_id,
            gpu_properties.name,
            gpu_properties.major,
            gpu_properties.minor,
            gpu_properties.total_memory / 1e9))
    else:
        print("Using CPU for inference.\n")

    ## Load the models one by one.
    if not args.griffin_lim:
        print("Preparing the encoder, the synthesizer and the vocoder...")
    else:
        print("Preparing the encoder and the synthesizer...")
    ensure_default_models(args.run_id, Path("saved_models"))
    encoder.inference.load_model(list(args.models_dir.glob(f"{args.run_id}/encoder.pt"))[0])
    synthesizer = Synthesizer(list(args.models_dir.glob(f"{args.run_id}/synthesizer.pt"))[0])
    if not args.griffin_lim:
        vocoder.load_model(list(args.models_dir.glob(f"{args.run_id}/vocoder.pt"))[0])


    # ## Run a test
    # print("Testing your configuration with small inputs.")
    # # Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
    # # sampling rate, which may differ.
    # # If you're unfamiliar with digital audio, know that it is encoded as an array of floats
    # # (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
    # # The sampling rate is the number of values (samples) recorded per second, it is set to
    # # 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond
    # # to an audio of 1 second.
    # print("\tTesting the encoder...")
    # encoder.embed_utterance(np.zeros(encoder.sampling_rate))

    # # Create a dummy embedding. You would normally use the embedding that encoder.embed_utterance
    # # returns, but here we're going to make one ourselves just for the sake of showing that it's
    # # possible.
    # embed = np.random.rand(speaker_embedding_size)
    # # Embeddings are L2-normalized (this isn't important here, but if you want to make your own
    # # embeddings it will be).
    # embed /= np.linalg.norm(embed)
    # # The synthesizer can handle multiple inputs with batching. Let's create another embedding to
    # # illustrate that
    # embeds = [embed, np.zeros(speaker_embedding_size)]
    # texts = ["test 1", "test 2"]
    # print("\tTesting the synthesizer... (loading the model will output a lot of text)")
    # mels = synthesizer.synthesize_spectrograms(texts, embeds)

    # # The vocoder synthesizes one waveform at a time, but it's more efficient for long ones. We
    # # can concatenate the mel spectrograms to a single one.
    # mel = np.concatenate(mels, axis=1)
    # # The vocoder can take a callback function to display the generation. More on that later. For
    # # now we'll simply hide it like this:
    # if not args.griffin_lim:
    #     no_action = lambda *args: None
    #     print("\tTesting the vocoder...")
    #     # For the sake of making this test short, we'll pass a short target length. The target length
    #     # is the length of the wav segments that are processed in parallel. E.g. for audio sampled
    #     # at 16000 Hertz, a target length of 8000 means that the target audio will be cut in chunks of
    #     # 0.5 seconds which will all be generated together. The parameters here are absurdly short, and
    #     # that has a detrimental effect on the quality of the audio. The default parameters are
    #     # recommended in general.
    #     vocoder.infer_waveform(mel, target=200, overlap=50, progress_callback=no_action)

    # print("All test passed! You can now synthesize speech.\n\n")


    ## Interactive speech generation
    print("This is a GUI-less example of interface to SV2TTS. The purpose of this script is to "
          "show how you can interface this project easily with your own. See the source code for "
          "an explanation of what is happening.\n")

    print("Interactive generation loop")
    num_generated = 0

    nlp = spacy.load('en_core_web_sm')
    weight = arg_dict["weight"] # 声音美颜的用户语音权重
    amp = 1

    # try:
    # Get the reference audio filepath
    # enter the number of reference audios
    # message1 = "Please enter the number of reference audios:\n"
    # num_of_input_audio = int(input(message1))
    num_of_input_audio = 1

    fpaths = [
        "/home/liuhaozhe/signal_processing_projs/collected_audios/openvoice_official/VOVO.mp3",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/openvoice_official/pleasant.mp3",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/openvoice_official/professional.mp3",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/recorded_audios/long_audios/liuhaozhe.m4a",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/recorded_audios/long_audios/dengmeng.m4a",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/recorded_audios/long_audios/wangqiuyu.m4a",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/celeb_audios/trimmed/Morgan_Freeman_trim.mp3",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/celeb_audios/trimmed/Beckham_trim.wav",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/celeb_audios/trimmed/Angelina_Jolie2.mp3",
        "/home/liuhaozhe/signal_processing_projs/collected_audios/celeb_audios/trimmed/emma_watson_trim.wav"
    ]

    for in_fpath in fpaths:

        for i in range(num_of_input_audio):
            # Computing the embedding
            # First, we load the wav using the function that the speaker encoder provides. This is
            # important: there is preprocessing that must be applied.

            # The following two methods are equivalent:
            # - Directly load from the filepath:
            # preprocessed_wav = encoder.preprocess_wav(in_fpath)
            # - If the wav is already loaded:

            # get duration info from input audio
            message2 = "Reference voice: enter an audio folder of a voice to be cloned (mp3, " \
                    f"wav, m4a, flac, ...):({i+1}/{num_of_input_audio})\n"
            # in_fpath = Path(input(message2).replace("\"", "").replace("\'", ""))

            # in_fpath = Path("/home/liuhaozhe/signal_processing_projs/collected_audios/celeb_audios/trimmed/emma_watson_trim.wav")

            fpath_without_ext = os.path.splitext(str(in_fpath))[0]
            speaker_name = os.path.normpath(fpath_without_ext).split(os.sep)[-1]

            is_wav_file, single_wav, wav_path = TransFormat(in_fpath, 'wav')

            if not is_wav_file:
                os.remove(wav_path)  # remove intermediate wav files
            # merge
            if i == 0:
                wav = single_wav
            else:
                wav = np.append(wav, single_wav)
        # write to disk
        path_ori, _ = os.path.split(wav_path)
        file_ori = 'temp.wav'
        fpath = os.path.join(path_ori, file_ori)
        sf.write(fpath, wav, samplerate=encoder.params_data.sampling_rate)

        # adjust the speed
        totDur_ori, nPause_ori, arDur_ori, nSyl_ori, arRate_ori = AudioAnalysis(path_ori, file_ori)
        DelFile(path_ori, '.TextGrid')
        os.remove(fpath)

        preprocessed_wav = encoder.inference.preprocess_wav(wav)

        print("Loaded input audio file succesfully")

        # Then we derive the embedding. There are many functions and parameters that the
        # speaker encoder interfaces. These are mostly for in-depth research. You will typically
        # only use this function (with its default parameters):
        input_embed = encoder.inference.embed_utterance(preprocessed_wav)
        # Choose standard audio

        fft_max_freq = vocoder.get_dominant_freq(preprocessed_wav)
        print(f"\nthe dominant frequency of input audio is {fft_max_freq}Hz")
        if fft_max_freq < encoder.params_data.split_freq:
            vocoder.hp.sex = 1
            standard_fpath = "standard_audios/male_1.wav"
        else:
            vocoder.hp.sex = 0
            standard_fpath = "standard_audios/female_1.wav"

        if os.path.exists(standard_fpath):
            
            standard_wav = Synthesizer.load_preprocess_wav(standard_fpath)
            preprocessed_standard_wav = encoder.inference.preprocess_wav(standard_wav)
            print("Loaded standard audio file successfully")

            standard_embed = encoder.inference.embed_utterance(preprocessed_standard_wav)

            embed1=np.copy(input_embed).dot(weight)
            embed2=np.copy(standard_embed).dot(1 - weight)
            embed=embed1+embed2
        else: 
            embed = np.copy(input_embed)

        embed[embed < encoder.params_data.set_zero_thres]=0 # 噪声值置零
        embed = embed * amp

        start_syn = time.time()
        # Generating the spectrogram
        # text = input("Write a sentence to be synthesized:\n")
        text = "Mechanics is an essential branch of physics that provides a framework for understanding the behavior of physical bodies under the influence of various forces. The principles of mechanics are based on the laws of motion, which form the foundation of the field. Mechanics has many practical applications in engineering and technology, from aerospace and automotive engineering to robotics and manufacturing. As science and technology continue to evolve, the principles of mechanics will remain an important part of our understanding of the physical world."

        # If seed is specified, reset torch seed and force synthesizer reload
        if args.seed is not None:
            torch.manual_seed(args.seed)
            synthesizer = Synthesizer(args.syn_model_fpath)

        # The synthesizer works in batch, so you need to put your data in a list or numpy array
        def preprocess_text(text):
            text = add_breaks(text) 
            text = english_cleaners(text)
            texts = [i.text.strip() for i in nlp(text).sents]  # split paragraph to sentences
            return texts

        texts = preprocess_text(text)
        print(f"the list of inputs texts:\n{texts}")

        # embeds = [embed] * len(texts)
        specs = []
        alignments = []
        stop_tokens = []
        for i, text in enumerate(texts):
            print(f"No.{i} sequence is {text}")
            spec, align, stop_token = synthesizer.synthesize_spectrograms([text], [embed], require_visualization=True)
            specs.append(spec[0])
            alignments.append(align[0])
            stop_tokens.append(stop_token[0])

        breaks = [spec.shape[1] for spec in specs]
        spec = np.concatenate(specs, axis=1)
        

        ## Save synthesizer visualization results
        if not os.path.exists("syn_results"):
            os.mkdir("syn_results")
        # save_attention(alignments, "syn_results/attention")
        # save_stop_tokens(stop_tokens, "syn_results/stop_tokens")
        # save_spectrogram(spec, "syn_results/mel")
        print("Created the mel spectrogram")

        end_syn = time.time()
        print(f"Prediction time of synthesizer is {end_syn - start_syn}s")

        start_voc = time.time()
        ## Generating the waveform
        print("Synthesizing the waveform:")

        # If seed is specified, reset torch seed and reload vocoder
        if args.seed is not None:
            torch.manual_seed(args.seed)
            vocoder.load_model(args.voc_model_fpath)

        # Synthesizing the waveform is fairly straightforward. Remember that the longer the
        # spectrogram, the more time-efficient the vocoder.
        if not args.griffin_lim:
            wav = vocoder.infer_waveform(spec)
        else:
            wav = Synthesizer.griffin_lim(spec)

        end_voc = time.time()
        print(f"Prediction time of vocoder is {end_voc - start_voc}s")
        print(f"Prediction time of TTS is {end_voc - start_syn}s")

        # Add breaks
        b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
        b_starts = np.concatenate(([0], b_ends[:-1]))
        wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
        breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
        wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])

        # Trim excess silences to compensate for gaps in spectrograms (issue #53)
        generated_wav = encoder.inference.preprocess_wav(wav)
        wav = wav / np.abs(wav).max() * 4

        # Save it on the disk
        # filename = "demo_output_%02d.wav" % num_generated
        if not os.path.exists("out_audios"):
            os.mkdir("out_audios")
        
        dir_path = os.path.dirname(os.path.realpath(__file__))  # current dir 
        filename = os.path.join(dir_path, f"out_audios/{speaker_name}_syn.wav")
        # print(wav.dtype)
        sf.write(filename, wav.astype(np.float32), synthesizer.sample_rate)
        num_generated += 1
        print("\nSaved output (havent't change speed) as %s\n\n" % filename)

        # Fix Speed(generate new audio)
        fix_file = work(totDur_ori, 
                        nPause_ori, 
                        arDur_ori, 
                        nSyl_ori, 
                        arRate_ori, 
                        filename)
        print(f"\nSaved output (fixed speed) as {fix_file}\n\n")


        # # Play the audio (non-blocking)
        # if not args.no_sound:
        #     import sounddevice as sd
        #     try:
        #         sd.stop()
        #         sd.play(wav, synthesizer.sample_rate)
        #     except sd.PortAudioError as e:
        #         print("\nCaught exception: %s" % repr(e))
        #         print("Continuing without audio playback. Suppress this message with the \"--no_sound\" flag.\n")
        #     except:
        #         raise


        # except Exception as e:
        #     print("Caught exception: %s" % repr(e))
        #     print("Restarting\n")