Add files via upload

training/data.py

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+import random
+import requests
+import os, glob
+
+# english literature
+books = [
+     'https://www.gutenberg.org/cache/epub/1513/pg1513.txt',
+     'https://www.gutenberg.org/files/2701/2701-0.txt',
+     'https://www.gutenberg.org/cache/epub/84/pg84.txt',
+     'https://www.gutenberg.org/cache/epub/2641/pg2641.txt',
+     'https://www.gutenberg.org/cache/epub/1342/pg1342.txt',
+     'https://www.gutenberg.org/cache/epub/100/pg100.txt'
+ ]
+
+#default english
+# allowed_chars = ' abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&*()-_+=\"\':;[]{}/<>,.`~\n\\'
+
+#german
+allowed_chars = ' aäbcdefghijklmnoöpqrsßtuüvwxyzABCDEFGHIJKLMNOÖPQRSTUÜVWXYZ0123456789!@#$%^&*()-_+=\"\':;[]{}/<>,.`~\n\\'
+
+
+def download_book(book):
+    return requests.get(book).content.decode('utf-8')
+
+
+def filter_data(data):
+    print('Filtering data')
+    return ''.join([char for char in data if char in allowed_chars])
+
+
+def load_books(fromfolder=False):
+    text_data = []
+    if fromfolder:
+        current_working_directory = os.getcwd()
+        print(current_working_directory)
+        path = 'text'
+        for filename in glob.glob(os.path.join(path, '*.txt')):
+            with open(os.path.join(os.getcwd(), filename), 'r') as f: # open in readonly mode
+                print(f'Loading {filename}')
+                text_data.append(filter_data(str(f.read())))
+    else:
+        print(f'Loading {len(books)} books into ram')
+        for book in books:
+            text_data.append(filter_data(str(download_book(book))))
+    print('Loaded books')
+    return ' '.join(text_data)
+
+
+def random_split_chunk(data, size=14):
+    data = data.split(' ')
+    index = random.randrange(0, len(data))
+    return ' '.join(data[index:index+size])

training/train.py

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+import os
+import fnmatch
+import shutil
+
+import numpy
+import torchaudio
+import gradio
+
+from bark.hubert.pre_kmeans_hubert import CustomHubert
+from bark.hubert.customtokenizer import auto_train
+from tqdm.auto import tqdm
+
+
+def training_prepare_files(path, model,progress=gradio.Progress(track_tqdm=True)):
+
+    semanticsfolder = "./training/data/output"
+    wavfolder = "./training/data/output_wav"
+    ready = os.path.join(path, 'ready')
+
+    testfiles = fnmatch.filter(os.listdir(ready), '*.npy')
+    if(len(testfiles) < 1):
+        # prepare and copy for training
+        hubert_model = CustomHubert(checkpoint_path=model)
+
+        wavfiles = fnmatch.filter(os.listdir(wavfolder), '*.wav')
+        for i, f in tqdm(enumerate(wavfiles), total=len(wavfiles)):
+            semaname = '.'.join(f.split('.')[:-1])  # Cut off the extension
+            semaname = f'{semaname}.npy'
+            semafilename =  os.path.join(semanticsfolder, semaname)
+            if not os.path.isfile(semafilename):
+                print(f'Skipping {f} no semantics pair found!')
+                continue
+
+            print('Processing', f)
+            wav, sr = torchaudio.load(os.path.join(wavfolder, f))
+            if wav.shape[0] == 2:  # Stereo to mono if needed
+                wav = wav.mean(0, keepdim=True)
+            output = hubert_model.forward(wav, input_sample_hz=sr)
+            out_array = output.cpu().numpy()
+            fname = f'{i}_semantic_features.npy'
+            numpy.save(os.path.join(ready, fname), out_array)
+            fname = f'{i}_semantic.npy'
+            shutil.copy(semafilename, os.path.join(ready, fname))
+
+def train(path, save_every, max_epochs):
+    auto_train(path, save_epochs=save_every)
+

training/training_prepare.py

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+import random
+import uuid
+import numpy
+import os
+import random
+import fnmatch
+
+from tqdm.auto import tqdm
+from scipy.io import wavfile
+
+from bark.generation import load_model, SAMPLE_RATE
+from bark.api import semantic_to_waveform
+
+from bark import text_to_semantic
+from bark.generation import load_model
+
+from training.data import load_books, random_split_chunk
+
+output = 'training/data/output'
+output_wav = 'training/data/output_wav'
+
+
+def prepare_semantics_from_text(num_generations):
+    loaded_data = load_books(True)
+
+    print('Loading semantics model')
+    load_model(use_gpu=True, use_small=False, force_reload=False, model_type='text')
+
+    if not os.path.isdir(output):
+        os.mkdir(output)
+
+    loop = 1
+    while 1:
+        filename = uuid.uuid4().hex + '.npy'
+        file_name = os.path.join(output, filename)
+        text = ''
+        while not len(text) > 0:
+            text = random_split_chunk(loaded_data)  # Obtain a short chunk of text
+            text = text.strip()
+        print(f'{loop} Generating semantics for text:', text)
+        loop+=1 
+        semantics = text_to_semantic(text, temp=round(random.uniform(0.6, 0.8), ndigits=2))
+        numpy.save(file_name, semantics)
+
+
+def prepare_wavs_from_semantics():
+    if not os.path.isdir(output):
+        raise Exception('No \'output\' folder, make sure you run create_data.py first!')
+    if not os.path.isdir(output_wav):
+        os.mkdir(output_wav)
+
+    print('Loading coarse model')
+    load_model(use_gpu=True, use_small=False, force_reload=False, model_type='coarse')
+    print('Loading fine model')
+    load_model(use_gpu=True, use_small=False, force_reload=False, model_type='fine')
+
+    files = fnmatch.filter(os.listdir(output), '*.npy')
+    current = 1
+    total = len(files)
+
+    for i, f in tqdm(enumerate(files), total=len(files)):
+        real_name = '.'.join(f.split('.')[:-1])  # Cut off the extension
+        file_name = os.path.join(output, f)
+        out_file = os.path.join(output_wav, f'{real_name}.wav')
+        if not os.path.isfile(out_file) and os.path.isfile(file_name):  # Don't process files that have already been processed, to be able to continue previous generations
+            print(f'Processing ({i+1}/{total}) -> {f}')
+            wav = semantic_to_waveform(numpy.load(file_name), temp=round(random.uniform(0.6, 0.8), ndigits=2))
+            # Change to PCM16
+            # wav = (wav * 32767).astype(np.int16)
+            wavfile.write(out_file, SAMPLE_RATE, wav)
+
+    print('Done!')
+