mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Switch to using logger instead of print()
This commit is contained in:
Brandon Thomas
@@ -0,0 +1,17 @@
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import logging
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def create_logger(logger_name: str):
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logger = logging.getLogger(logger_name)
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logger.setLevel(logging.INFO)
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handler = logging.StreamHandler()
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handler.setLevel(logging.INFO)
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formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] %(message)s", "%Y-%m-%d %H:%M:%S")
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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return logger
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LOGGER = create_logger("fakeyou")
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+28
-25
@@ -35,6 +35,8 @@ from text.pipeline.legacy_fakeyou_pipeline import LegacyFakeYouPipeline
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from text.pipeline_components.phonetization.cmudict import DEFAULT_CMUDICT_PATH, CMUDict
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from text.pipeline_components.phonetization.english_phonetization import EnglishPhonetization
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from logger import LOGGER
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def print_gpu_info():
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print('========================================')
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print('Python interpreter', sys.executable)
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@@ -57,7 +59,7 @@ def load_tacotron_model(checkpoint_path):
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hparams.max_decoder_steps = 1000 # How long the audio will be before it cuts off (1000 is about 11 seconds)
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hparams.gate_threshold = 0.1 # Model must be 90% sure the clip is over before ending generation (the higher this number is, the more likely that the AI will keep generating until it reaches the Max Decoder Steps)
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model = Tacotron2(hparams)
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print('Loading synthesizer at path: {}'.format(checkpoint_path))
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LOGGER.info('Loading synthesizer at path: {}'.format(checkpoint_path))
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model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
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_ = model.cuda().eval().half()
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# NB(bt, 2021-05-31): Trying to get everything on the same device
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@@ -122,7 +124,6 @@ def preprocess_text_old(text, raw_input=True):
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pass
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pre_sequence += line
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print(pre_sequence)
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with torch.no_grad():
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# save VRAM by not including gradients
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sequence = np.array(text_to_sequence(pre_sequence, ['english_cleaners']))[None, :]
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@@ -213,10 +214,10 @@ class TacotronWaveglowPipeline:
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def maybe_load_hifigan(self, checkpoint_path):
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if self.hifigan_filename == checkpoint_path:
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print('Hifi-gan model already loaded into memory: {}'.format(checkpoint_path))
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LOGGER.info('Hifi-gan model already loaded into memory: {}'.format(checkpoint_path))
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return
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print('Dumping previous Hifi-gan model from memory: {}'.format(self.hifigan_filename))
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print('Loading Hifi-gan model into memory: {}'.format(checkpoint_path), flush=True)
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LOGGER.info('Dumping previous Hifi-gan model from memory: {}'.format(self.hifigan_filename))
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LOGGER.info('Loading Hifi-gan model into memory: {}'.format(checkpoint_path), flush=True)
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hifigan, h, denoiser = load_hifigan_model(checkpoint_path, False)
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self.hifigan = hifigan
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self.hifigan_denoiser = denoiser
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@@ -225,10 +226,10 @@ class TacotronWaveglowPipeline:
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def maybe_load_hifigan_super_resolution(self, checkpoint_path):
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if self.hifigan_super_resolution_filename == checkpoint_path:
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print('Hifi-gan (SuperRes) model already loaded into memory: {}'.format(checkpoint_path))
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LOGGER.info('Hifi-gan (SuperRes) model already loaded into memory: {}'.format(checkpoint_path))
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return
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print('Dumping previous Hifi-gan (SuperRes) model from memory: {}'.format(self.hifigan_super_resolution_filename))
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print('Loading Hifi-gan (SuperRes) model into memory: {}'.format(checkpoint_path), flush=True)
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LOGGER.info('Dumping previous Hifi-gan (SuperRes) model from memory: {}'.format(self.hifigan_super_resolution_filename))
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LOGGER.info('Loading Hifi-gan (SuperRes) model into memory: {}'.format(checkpoint_path), flush=True)
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hifigan, h, denoiser = load_hifigan_model(checkpoint_path, True)
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self.hifigan_super_resolution = hifigan
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self.hifigan_super_resolution_denoiser = denoiser
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@@ -237,28 +238,28 @@ class TacotronWaveglowPipeline:
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def maybe_load_waveglow_model(self, checkpoint_path):
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if self.waveglow_filename == checkpoint_path:
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print('Waveglow model already loaded into memory: {}'.format(checkpoint_path))
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LOGGER.info('Waveglow model already loaded into memory: {}'.format(checkpoint_path))
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return
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print('Dumping previous waveglow model from memory: {}'.format(self.waveglow_filename))
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print('Loading waveglow model into memory: {}'.format(checkpoint_path), flush=True)
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LOGGER.info('Dumping previous waveglow model from memory: {}'.format(self.waveglow_filename))
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LOGGER.info('Loading waveglow model into memory: {}'.format(checkpoint_path), flush=True)
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self.waveglow = load_waveglow_model(checkpoint_path)
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self.waveglow_filename = checkpoint_path
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def maybe_load_tacotron_model_to_cache(self, checkpoint_path):
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if checkpoint_path in self.tacotron_model_cache:
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print('Tacotron model already loaded into memory: {}'.format(checkpoint_path))
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LOGGER.info('Tacotron model already loaded into memory: {}'.format(checkpoint_path))
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return
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print('Loading tacotron model into memory: {}'.format(checkpoint_path), flush=True)
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LOGGER.info('Loading tacotron model into memory: {}'.format(checkpoint_path), flush=True)
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model = load_tacotron_model(checkpoint_path)
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self.tacotron_model_cache[checkpoint_path] = model
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print('Now {} synthesizer models in memory'.format(len(self.tacotron_model_cache)))
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LOGGER.info('Now {} synthesizer models in memory'.format(len(self.tacotron_model_cache)))
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def uncache_tacotron_model(self, checkpoint_path):
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print('Currently {} synthesizer models in memory'.format(
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LOGGER.info('Currently {} synthesizer models in memory'.format(
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len(self.tacotron_model_cache)))
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print('Clearing synthesizer from memory: {}'.format(checkpoint_path))
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LOGGER.info('Clearing synthesizer from memory: {}'.format(checkpoint_path))
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self.tacotron_model_cache.pop(checkpoint_path, None)
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print('Now reduced to {} synthesizer models in memory'.format(
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LOGGER.info('Now reduced to {} synthesizer models in memory'.format(
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len(self.tacotron_model_cache)))
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def infer(self, args):
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@@ -270,12 +271,14 @@ class TacotronWaveglowPipeline:
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assert('output_spectrogram_filename' in args)
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assert('output_metadata_filename' in args)
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print('Raw text: {}'.format(args['raw_text']))
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LOGGER.info(f"raw inference text: {args['raw_text']}")
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sequence = preprocess_text(args['raw_text'], args['text_pipeline_type'])
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tacotron = self.tacotron_model_cache[args['synthesizer_checkpoint_path']]
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LOGGER.info("running inference...")
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with torch.no_grad():
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mel_outputs, mel_outputs_postnet, _, alignments = tacotron.inference(sequence)
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@@ -286,23 +289,23 @@ class TacotronWaveglowPipeline:
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else:
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raise Exception('Wrong vocoder type: {}'.format(args['vocoder_type']))
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print('Saving spectrogram as JSON...')
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LOGGER.info('Saving spectrogram as JSON...')
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save_spectorgram_json_file(mel_outputs_postnet, args['output_spectrogram_filename'])
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print('Generating metadata file...')
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LOGGER.info('Generating metadata file...')
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generate_metadata_file(args['output_audio_filename'], args['output_metadata_filename'])
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def vocode_waveglow(self, args, mel_outputs_postnet):
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print('Running waveglow...')
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LOGGER.info('Running waveglow...')
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with torch.no_grad():
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sigma = 0.8
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audio = self.waveglow.infer(mel_outputs_postnet, sigma=sigma)
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print('Encoding and saving audio...')
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LOGGER.info('Encoding and saving audio...')
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save_wav_audio_file(audio, args['output_audio_filename'])
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def vocode_hifigan_superres(self, args, mel_outputs_postnet):
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print('Running hifigan...')
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LOGGER.info('Running hifigan...')
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with torch.no_grad():
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y_g_hat = self.hifigan(mel_outputs_postnet.float())
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@@ -327,7 +330,7 @@ class TacotronWaveglowPipeline:
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wave_out = wave.astype(np.int16)
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# HiFi-GAN super-resolution
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print('Running hifigan super resolution...')
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LOGGER.info('Running hifigan super resolution...')
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wave = wave / MAX_WAV_VALUE
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wave = torch.FloatTensor(wave).to(torch.device("cuda"))
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new_mel = mel_spectrogram(
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@@ -359,7 +362,7 @@ class TacotronWaveglowPipeline:
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sr_mix = wave_out + y_padded
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sr_mix = sr_mix / normalize
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print('Encoding and saving audio...')
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LOGGER.info('Encoding and saving audio...')
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rate = self.hifigan_super_resolution_h.sampling_rate
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output_audio = sr_mix.astype(np.int16)
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write_wav(args['output_audio_filename'], rate, output_audio)
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+14
-1
@@ -30,6 +30,8 @@ from vocodes_common import load_tacotron_model
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from vocodes_common import load_waveglow_model
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from vocodes_common import load_hifigan_model
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from logger import LOGGER
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print("TensorFlow version: {}".format(tf.version.VERSION))
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print_gpu_info()
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@@ -204,10 +206,18 @@ class IndexHandler():
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response.content_type = 'text/html'
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response.body = INDEX_HTML
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class StatusHandler():
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def on_get(self, request, response):
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LOGGER.info("/_status endpoint requested.")
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response.content_type = 'application/json'
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response.body = '{"status": "ok"}'
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class ApiHandler():
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def on_post(self, request, response):
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raw_data = json.load(request.bounded_stream)
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LOGGER.info(f"TTS Request: {raw_data}")
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# Request parameters
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synthesizer_checkpoint_path = raw_data.get('synthesizer_checkpoint_path')
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text_pipeline_type = raw_data.get('text_pipeline_type')
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@@ -256,6 +266,8 @@ class ApiHandler():
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'output_metadata_filename': output_metadata_filename,
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}
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LOGGER.info(f"running inference with args: {inference_args}")
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pipeline.infer(inference_args)
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def main():
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@@ -265,9 +277,10 @@ def main():
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api = falcon.API(middleware=[MultipartMiddleware()])
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api.add_route('/', IndexHandler())
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api.add_route('/_status', StatusHandler())
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api.add_route('/infer', ApiHandler())
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print('Serving on 0.0.0.0:%d' % args.port)
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LOGGER.info(f"Starting server on 0.0.0.0:{args.port}")
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simple_server.make_server('0.0.0.0', args.port, api).serve_forever()
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if __name__ == '__main__':
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