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chore(.gitignore): add bark_cache directory to gitignore

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feat(video.py): use wav format instead of mp3 for generated audio files
feat(montage.py): use Bark TTS instead of 🐸TTS
feat(speak.py): add support for Bark TTS
fix(speak.py): remove unused 🐸TTS import and variable
fix(main.py): fix asyncio.run() call placement
docs: update requirements.txt with new dependencies
This commit is contained in:
Paillat
2023-06-25 21:59:52 +02:00
parent 5498fb7bac
commit 866f1b4138
6 changed files with 74 additions and 30 deletions
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64
generators/speak.py
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@@ -1,9 +1,6 @@
from TTS.api import TTS
# Running a multi-speaker and multi-lingual model
import os
# List available 🐸TTS models and choose the first one
model_best_multi = "tts_models/en/vctk/vits"
fakenames = {
"Alexander": "p230",
"Benjamin": "p240",
@@ -14,17 +11,52 @@ fakenames = {
voices = ["Alexander", "Benjamin", "Amelia", "Katherine", "Johanne"]
# Init TTS
def generate_voice(path, text, speaker="Alexander"):
model = model_best_multi
speaker = fakenames[speaker] if speaker in fakenames else speaker
print(f"Generating voice for {model} with speaker {speaker}")
try:
tts = TTS(model, gpu=True)
except:
tts = TTS(model, gpu=False)
tts.tts_to_file(text=text, file_path=path, speaker=speaker, speed=1, emotion="Happy")
class VoiceGenerator:
def __init__(self, mode="Bark", speaker=""):
self.mode = mode
self.speaker = speaker
if mode == "Bark":
os.environ["XDG_CACHE_HOME"] = os.path.join(os.getcwd(), "bark_cache")
from bark import preload_models, generation
preload_models()
self.speaker = "v2/en_speaker_6"
else:
from TTS.api import TTS
model = "tts_models/en/vctk/vits"
self.speaker = fakenames[speaker] if speaker in fakenames else speaker
print(f"Generating voice for {model} with speaker {speaker}")
try:
self.tts = TTS(model, gpu=True)
except:
self.tts = TTS(model, gpu=False)
if self.speaker == "": self.speaker = "p230"
else:
self.speaker = fakenames[self.speaker] if self.speaker in fakenames else fakenames["Alexander"]
def generate_voice(self, path, text):
if self.mode == "Bark":
from bark import SAMPLE_RATE, generate_audio, preload_models
from scipy.io.wavfile import read as wavread, write as wavwrite
import noisereduce as nr
import soundfile
import numpy as np
import nltk
sentences = nltk.sent_tokenize(text)
pieces = []
silence = np.zeros(int(0.25 * SAMPLE_RATE)) # quarter second of silence
for sentence in sentences:
audio_array = generate_audio(sentence, history_prompt=self.speaker)
pieces += [audio_array, silence.copy()]
audio_array = np.concatenate(pieces)
soundfile.write(path, audio_array, SAMPLE_RATE, format="WAV", subtype="PCM_16")
rate, data = wavread(path)
reduced_noise = nr.reduce_noise(y=data, sr=rate)
os.remove(path)
wavwrite(path, rate, reduced_noise)
else:
self.tts.tts_to_file(text=text, file_path=path, speaker=self.speaker, speed=1, emotion="Happy")
if __name__ == "__main__":
generate_voice("test/test.mp3", "This is a test. I like the words python, django and flask. Betty bought a bit of butter but the butter was bitter. So she bought some better butter to make the bitter butter better.")
generator = VoiceGenerator()
generator.generate_voice("test/test_r.wav", "Hello there!")
generator.generate_voice("test/teste_r.wav", "This is a test. I like the words python, django and flask. Betty bought a bit of butter but the butter was bitter. So she bought some better butter to make the bitter butter better.")