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import os |
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import pytest |
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from tests.utils import wrap_test_forked |
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from src.tts_sentence_parsing import init_sentence_state |
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from tests.test_sentence_parsing import bot_list |
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@pytest.mark.audio |
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@wrap_test_forked |
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def test_sentence_to_wave(): |
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os.environ['CUDA_HOME'] = '/usr/local/cuda-11.7' |
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from src.tts_coqui import sentence_to_wave, get_xtt, get_latent, get_role_to_wave_map |
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chatbot_role = "Female AI Assistant" |
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sentence = "I am an AI assistant. I can help you with any tasks." |
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tts_speed = 1.0 |
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model, supported_languages = get_xtt() |
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latent = get_latent(get_role_to_wave_map()[chatbot_role], model=model) |
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generated_speech = sentence_to_wave(sentence, |
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supported_languages, |
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tts_speed, |
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latent=latent, |
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model=model, |
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return_as_byte=False, |
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return_nonbyte_as_file=True, |
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return_gradio=False) |
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print(generated_speech, flush=True) |
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import wave |
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with wave.open(generated_speech, mode='rb') as f: |
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pass |
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@pytest.mark.audio |
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@wrap_test_forked |
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def test_generate_speech(): |
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os.environ['CUDA_HOME'] = '/usr/local/cuda-11.7' |
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from src.tts_coqui import generate_speech, get_xtt, get_latent, get_role_to_wave_map |
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chatbot_role = "Female AI Assistant" |
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model, supported_languages = get_xtt() |
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latent = get_latent(get_role_to_wave_map()[chatbot_role], model=model) |
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response = 'I am an AI assistant. What do you want from me? I am very busy.' |
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for char in response: |
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generate_speech(char, model=model, supported_languages=supported_languages, latent=latent) |
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@pytest.mark.audio |
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@wrap_test_forked |
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def test_full_generate_speech(): |
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os.environ['CUDA_HOME'] = '/usr/local/cuda-11.7' |
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from src.tts_coqui import generate_speech, get_xtt, get_latent, get_role_to_wave_map |
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bot = 'I am an AI assistant. What do you want from me? I am very busy.' |
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def response_gen(): |
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for word1 in bot.split(' '): |
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yield word1 |
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chatbot_role = "Female AI Assistant" |
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model, supported_languages = get_xtt() |
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latent = get_latent(get_role_to_wave_map()[chatbot_role], model=model) |
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response = "" |
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sentence_state = init_sentence_state() |
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sentences = [] |
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audios = [] |
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sentences_expected = ['I am an AI assistant.', 'What do you want from me?', 'I am very busy.'] |
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for word in response_gen(): |
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response += word + ' ' |
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audio, sentence, sentence_state = \ |
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generate_speech(response, |
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model=model, |
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supported_languages=supported_languages, |
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latent=latent, |
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sentence_state=sentence_state, |
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return_as_byte=False, |
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return_nonbyte_as_file=True, |
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return_gradio=False, |
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is_final=False, verbose=True) |
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if sentence is not None: |
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print(sentence) |
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sentences.append(sentence) |
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if audio is not None: |
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audios.append(audio) |
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audio, sentence, sentence_state = \ |
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generate_speech(response, |
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model=model, |
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supported_languages=supported_languages, |
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latent=latent, |
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sentence_state=sentence_state, |
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return_as_byte=False, |
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return_nonbyte_as_file=True, |
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return_gradio=False, |
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is_final=True, verbose=True) |
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if sentence is not None: |
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print(sentence) |
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sentences.append(sentence) |
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if audio is not None: |
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audios.append(audio) |
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assert sentences == sentences_expected |
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assert len(sentences) == len(audios) |
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print(audios) |
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@pytest.mark.audio |
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@wrap_test_forked |
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@pytest.mark.parametrize("bot, sentences_expected", bot_list) |
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def test_predict_from_text(bot, sentences_expected): |
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speeches = [] |
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from src.tts import get_tts_model, get_speakers |
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processor, model, vocoder = get_tts_model() |
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speaker = get_speakers()[0] |
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tts_speed = 1.0 |
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from src.tts import predict_from_text |
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for audio in predict_from_text(bot, speaker, tts_speed, |
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processor=processor, model=model, vocoder=vocoder, |
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return_as_byte=False, |
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verbose=True): |
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if audio[1].shape[0] > 0: |
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speeches.append(audio) |
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assert len(speeches) == len(sentences_expected) |
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