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@ -54,19 +54,31 @@ def ort_predict(args):
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device=args.device,
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device=args.device,
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cpu_threads=args.cpu_threads)
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cpu_threads=args.cpu_threads)
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merge_sentences = True
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# frontend warmup
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# frontend warmup
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# Loading model cost 0.5+ seconds
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# Loading model cost 0.5+ seconds
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if args.lang == 'zh':
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if args.lang == 'zh':
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frontend.get_input_ids("你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=True)
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frontend.get_input_ids(
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"你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=merge_sentences)
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else:
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else:
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print("lang should in be 'zh' here!")
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frontend.get_input_ids(
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"hello, thank you, thank you very much",
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merge_sentences=merge_sentences)
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# am warmup
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# am warmup
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spk_id = [args.spk_id]
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for T in [27, 38, 54]:
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for T in [27, 38, 54]:
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am_input_feed = {}
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am_input_feed = {}
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if am_name == 'fastspeech2':
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if am_name == 'fastspeech2':
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phone_ids = np.random.randint(1, 266, size=(T, ))
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if args.lang == 'en':
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phone_ids = np.random.randint(1, 78, size=(T, ))
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else:
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phone_ids = np.random.randint(1, 266, size=(T, ))
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am_input_feed.update({'text': phone_ids})
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am_input_feed.update({'text': phone_ids})
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if am_dataset in {"aishell3", "vctk"}:
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am_input_feed.update({'spk_id': spk_id})
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elif am_name == 'speedyspeech':
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elif am_name == 'speedyspeech':
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phone_ids = np.random.randint(1, 92, size=(T, ))
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phone_ids = np.random.randint(1, 92, size=(T, ))
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tone_ids = np.random.randint(1, 5, size=(T, ))
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tone_ids = np.random.randint(1, 5, size=(T, ))
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@ -96,12 +108,18 @@ def ort_predict(args):
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phone_ids = input_ids["phone_ids"]
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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tone_ids = input_ids["tone_ids"]
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elif args.lang == 'en':
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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else:
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print("lang should in be 'zh' here!")
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print("lang should in {'zh', 'en'}!")
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# merge_sentences=True here, so we only use the first item of phone_ids
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# merge_sentences=True here, so we only use the first item of phone_ids
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phone_ids = phone_ids[0].numpy()
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phone_ids = phone_ids[0].numpy()
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if am_name == 'fastspeech2':
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if am_name == 'fastspeech2':
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am_input_feed.update({'text': phone_ids})
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am_input_feed.update({'text': phone_ids})
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if am_dataset in {"aishell3", "vctk"}:
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am_input_feed.update({'spk_id': spk_id})
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elif am_name == 'speedyspeech':
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elif am_name == 'speedyspeech':
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tone_ids = tone_ids[0].numpy()
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tone_ids = tone_ids[0].numpy()
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am_input_feed.update({'phones': phone_ids, 'tones': tone_ids})
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am_input_feed.update({'phones': phone_ids, 'tones': tone_ids})
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@ -130,19 +148,40 @@ def parse_args():
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'--am',
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'--am',
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type=str,
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type=str,
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default='fastspeech2_csmsc',
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default='fastspeech2_csmsc',
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choices=['fastspeech2_csmsc', 'speedyspeech_csmsc'],
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choices=[
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'fastspeech2_csmsc',
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'fastspeech2_aishell3',
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'fastspeech2_ljspeech',
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'fastspeech2_vctk',
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'speedyspeech_csmsc',
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],
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help='Choose acoustic model type of tts task.')
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# voc
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# voc
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parser.add_argument(
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parser.add_argument(
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'--voc',
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'--voc',
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type=str,
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type=str,
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default='hifigan_csmsc',
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default='hifigan_csmsc',
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choices=['hifigan_csmsc', 'mb_melgan_csmsc', 'pwgan_csmsc'],
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choices=[
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'pwgan_csmsc',
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'pwgan_aishell3',
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'pwgan_ljspeech',
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'pwgan_vctk',
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'hifigan_csmsc',
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'hifigan_aishell3',
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'hifigan_ljspeech',
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'hifigan_vctk',
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'mb_melgan_csmsc',
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],
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help='Choose vocoder type of tts task.')
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help='Choose vocoder type of tts task.')
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# other
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# other
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parser.add_argument(
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parser.add_argument(
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TianYuan
3 years ago