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demo_cli.py

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+from encoder.params_model import model_embedding_size as speaker_embedding_size
+from utils.argutils import print_args
+from utils.modelutils import check_model_paths
+from synthesizer.inference import Synthesizer
+from encoder import inference as encoder
+from vocoder import inference as vocoder
+from pathlib import Path
+import numpy as np
+import soundfile as sf
+import librosa
+import argparse
+import torch
+import sys
+import os
+from audioread.exceptions import NoBackendError
+
+if __name__ == '__main__':
+    ## Info & args
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument("-e", "--enc_model_fpath", type=Path, 
+                        default="encpretrained.pt",
+                        help="Path to a saved encoder")
+    parser.add_argument("-s", "--syn_model_fpath", type=Path, 
+                        default="synpretrained.pt",
+                        help="Path to a saved synthesizer")
+    parser.add_argument("-v", "--voc_model_fpath", type=Path, 
+                        default="vocpretrained.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.")
+    parser.add_argument("--no_mp3_support", action="store_true", help="If True, disallows loading mp3 files to prevent audioread errors when ffmpeg is not installed.")
+    parser.add_argument("-audio", "--audio_path", type=Path, required = True,
+                        help="Path to a audio file")
+    parser.add_argument("--text", type=str, required = True, help="Text Input")
+    args = parser.parse_args()
+    print_args(args, parser)
+    if not args.no_sound:
+        import sounddevice as sd
+
+    if args.cpu:
+        # Hide GPUs from Pytorch to force CPU processing
+        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+    if not args.no_mp3_support:
+        try:
+            librosa.load("samples/1320_00000.mp3")
+        except NoBackendError:
+            print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
+                  "Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
+            exit(-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")
+    
+    ## Remind the user to download pretrained models if needed
+    check_model_paths(encoder_path=args.enc_model_fpath,
+                      synthesizer_path=args.syn_model_fpath,
+                      vocoder_path=args.voc_model_fpath)
+    
+    ## Load the models one by one.
+    print("Preparing the encoder, the synthesizer and the vocoder...")
+    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 <sampling_rate> will always correspond 
+    # # to an audio of 1 second.
+    # print("	Testing 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("	Testing 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("	Testing 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")
+    # while True:
+    # 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 = args.audio_path
+
+    if in_fpath.suffix.lower() == ".mp3" and args.no_mp3_support:
+        print("Can't Use mp3 files please try again:")
+    ## 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 = args.text
+    
+    # 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)
+        
+    # Save it on the disk
+    filename = "demo_output_1.wav"
+    print(generated_wav.dtype)
+    sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate)
+    print("\nSaved output as %s\n\n" % filename)