modelscope/tests/pipelines/test_text_to_speech.py

import unittest

# NOTICE: Tensorflow 1.15 seems not so compatible with pytorch.
#         A segmentation fault may be raise by pytorch cpp library
#         if 'import tensorflow' in front of 'import torch'.
#         Puting a 'import torch' here can bypass this incompatibility.
import torch
from scipy.io.wavfile import write

from modelscope.metainfo import Pipelines, Preprocessors
from modelscope.models import Model
from modelscope.pipelines import pipeline
from modelscope.preprocessors import build_preprocessor
from modelscope.utils.constant import Fields, Tasks
from modelscope.utils.logger import get_logger
from modelscope.utils.test_utils import test_level

import tensorflow as tf  # isort:skip

logger = get_logger()


class TextToSpeechSambertHifigan16kPipelineTest(unittest.TestCase):

    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    def test_pipeline(self):
        text = '明天天气怎么样'
        preprocessor_model_id = 'damo/speech_binary_tts_frontend_resource'
        am_model_id = 'damo/speech_sambert16k_tts_zhitian_emo'
        voc_model_id = 'damo/speech_hifigan16k_tts_zhitian_emo'
        sambert_tts = pipeline(
            task=Tasks.text_to_speech,
            model=[preprocessor_model_id, am_model_id, voc_model_id])
        self.assertTrue(sambert_tts is not None)
        output = sambert_tts(text)
        self.assertTrue(len(output['output']) > 0)
        write('output.wav', 16000, output['output'])


if __name__ == '__main__':
    unittest.main()