import argparse import os from pathlib import Path import librosa import numpy as np import soundfile as sf import torch from encoder import inference as encoder from encoder.params_model import model_embedding_size as speaker_embedding_size from synthesizer.inference import Synthesizer from utils.argutils import print_args from utils.default_models import ensure_default_models from vocoder import inference as vocoder if __name__ == '__main__': parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("-e", "--enc_model_fpath", type=Path, default="saved_models/default/encoder.pt", help="Path to a saved encoder") parser.add_argument("-s", "--syn_model_fpath", type=Path, default="saved_models/default/synthesizer.pt", help="Path to a saved synthesizer") parser.add_argument("-v", "--voc_model_fpath", type=Path, default="saved_models/default/vocoder.pt", help="Path to a saved vocoder") 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") 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. print("Preparing the encoder, the synthesizer and the vocoder...") ensure_default_models(Path("saved_models")) encoder.load_model(args.enc_model_fpath) synthesizer = Synthesizer(args.syn_model_fpath) vocoder.load_model(args.voc_model_fpath) ## 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 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: 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 while True: try: # Get the reference audio filepath message = "Reference voice: enter an audio filepath of a voice to be cloned (mp3, " \ "wav, m4a, flac, ...):\n" in_fpath = Path(input(message).replace("\"", "").replace("\'", "")) ## 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: original_wav, sampling_rate = librosa.load(str(in_fpath)) preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate) print("Loaded 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): embed = encoder.embed_utterance(preprocessed_wav) print("Created the embedding") ## Generating the spectrogram text = input("Write a sentence (+-20 words) to be synthesized:\n") # 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 texts = [text] embeds = [embed] # If you know what the attention layer alignments are, you can retrieve them here by # passing return_alignments=True specs = synthesizer.synthesize_spectrograms(texts, embeds) spec = specs[0] print("Created the mel spectrogram") ## 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. generated_wav = vocoder.infer_waveform(spec) ## Post-generation # There's a bug with sounddevice that makes the audio cut one second earlier, so we # pad it. generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant") # Trim excess silences to compensate for gaps in spectrograms (issue #53) generated_wav = encoder.preprocess_wav(generated_wav) # Play the audio (non-blocking) if not args.no_sound: import sounddevice as sd try: sd.stop() sd.play(generated_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 # Save it on the disk filename = "demo_output_%02d.wav" % num_generated print(generated_wav.dtype) sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate) num_generated += 1 print("\nSaved output as %s\n\n" % filename) except Exception as e: print("Caught exception: %s" % repr(e)) print("Restarting\n")