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import IPython
from huggingface_hub.inference_api import InferenceApi
import torch
from TTS.api import TTS
import wave
import espeakng
import subprocess
from scipy.io import wavfile
from transformers import pipeline
import os
import numpy as np
def synth_mms(text:str, model:str):
'''
Use Huggingface inference pipeline to synthesize text.
(Can be replaced by inference API, but that requires stored API token.)
Inputs:
text: Text to synthesze
model: Model code of the form mms-tts-LAN
Returns:
Streaming numpy and sampling rate.
'''
#inference = InferenceApi(repo_id=f"facebook/{model}",
# token=API_TOKEN)
#mms_tts = inference(inputs=text,
# raw_response=True)._content
if model is not None:
pipe = pipeline("text-to-speech", model=model, device=-1) # Change device if it should use GPU
mms_tts = pipe(text)
return mms_tts['audio'], mms_tts['sampling_rate']
else:
return None
def synth_coqui(text:str, model:str):
'''
Use Coqui inference API to synthesize text.
Inputs:
text: Text to synthesze
model: Model code
Returns:
Streaming Wav and sampling rate.
IMPORTANT: Current implementation assumes 22050 sampling rate, this should be verified when adding a new model.
'''
if model is not None:
# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Init TTS
tts = TTS(model, progress_bar=False).to(device)
# Infer
wav = tts.tts(text=text) # is_multi_speaker=False
return np.array(wav), 22050
else:
return None
def synth_espeakng(text:str, model:str):
'''
Use ESpeak-NG to synthesize text.
Inputs:
text: Text to synthesze
model: Model code
Returns:
Streaming Wav and sampling rate.
'''
if model is not None:
subprocess.run(['espeak-ng', f'-v{model}', "-w test.wav", text])
#esng = espeakng.Speaker()
#esng.voice = model
#esng.say(text, export_path="test.wav")
sampling_rate, wav = wavfile.read('test.wav')
os.remove("test.wav")
#wav = tts.tts(text=text)
return wav, sampling_rate
else:
return None
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