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import unittest
import torch
from torchaudio.transforms import Resample
import pronunciationTrainer
from constants import sample_rate_start, sample_rate_resample, MODEL_NAME_DEFAULT
from lambdaSpeechToScore import audioread_load
from tests import EVENTS_FOLDER, set_seed
trainer_sst_lambda = {
'de': pronunciationTrainer.getTrainer("de"),
'en': pronunciationTrainer.getTrainer("en")
}
transform = Resample(orig_freq=sample_rate_start, new_freq=sample_rate_resample)
def helper_neural_asr(language: str, model_name: str):
import models as mo
set_seed()
signal, _ = audioread_load(EVENTS_FOLDER / f"test_{language}_easy.wav")
signal_transformed = transform(torch.Tensor(signal)).unsqueeze(0)
signal_transformed_preprocessed = pronunciationTrainer.preprocessAudioStandalone(signal_transformed)
asr_model = mo.getASRModel(language, model_name=model_name)
asr_model.processAudio(signal_transformed_preprocessed)
audio_transcript = asr_model.getTranscript()
word_locations_in_samples = asr_model.getWordLocations()
return audio_transcript, word_locations_in_samples
class TestNeuralASR(unittest.TestCase):
def setUp(self):
import platform
import os
if platform.system() == "Windows" or platform.system() == "Win32":
os.environ["PYTHONUTF8"] = "1"
os.environ["IS_TESTING"] = "TRUE"
def tearDown(self):
import platform
import os
if platform.system() == "Windows" or platform.system() == "Win32" and "PYTHONUTF8" in os.environ:
del os.environ["PYTHONUTF8"]
del os.environ["IS_TESTING"]
def test_neural_asr_de_whisper(self):
self.maxDiff = None
for model_name in [MODEL_NAME_DEFAULT, "whisper"]:
audio_transcript, word_locations_in_samples = helper_neural_asr("de", model_name)
assert audio_transcript == ' Hallo, wie geht es dir?'
self.assertEqual(word_locations_in_samples, [
{'end_ts': 5120.0, 'start_ts': 0.0, 'word': ' Hallo,'},
{'end_ts': 10240.0, 'start_ts': 8640.0, 'word': ' wie'},
{'end_ts': 13120.0, 'start_ts': 10240.0, 'word': ' geht'},
{'end_ts': 16640.0, 'start_ts': 13120.0, 'word': ' es'},
{'end_ts': 20160.0, 'start_ts': 16640.0, 'word': ' dir?'}
])
def test_neural_asr_en_default(self):
self.maxDiff = None
for model_name in [MODEL_NAME_DEFAULT, "whisper"]:
audio_transcript, word_locations_in_samples = helper_neural_asr("en", model_name)
assert audio_transcript == ' Hi there, how are you?'
self.assertEqual(word_locations_in_samples, [
{'end_ts': 2240.0, 'start_ts': 0.0, 'word': ' Hi'},
{'end_ts': 4800.0, 'start_ts': 2240.0, 'word': ' there,'},
{'end_ts': 9280.0, 'start_ts': 7360.0, 'word': ' how'},
{'end_ts': 11200.0, 'start_ts': 9280.0, 'word': ' are'},
{'end_ts': 13760.0, 'start_ts': 11200.0, 'word': ' you?'}
])
def test_neural_asr_de_faster_whisper(self):
self.maxDiff = None
audio_transcript, word_locations_in_samples = helper_neural_asr("de", "faster_whisper")
assert audio_transcript == ' Hallo, wie geht es dir?'
self.assertEqual(word_locations_in_samples, [
{'end_ts': 5120.0, 'start_ts': 0.0, 'word': ' Hallo,'},
{'end_ts': 10240.0, 'start_ts': 8640.0, 'word': ' wie'},
{'end_ts': 13120.0, 'start_ts': 10240.0, 'word': ' geht'},
{'end_ts': 16640.0, 'start_ts': 13120.0, 'word': ' es'},
{'end_ts': 20160.0, 'start_ts': 16640.0, 'word': ' dir?'}
])
def test_neural_asr_en_faster_whisper(self):
self.maxDiff = None
audio_transcript, word_locations_in_samples = helper_neural_asr("en", "faster_whisper")
assert audio_transcript == ' Hi there, how are you?'
self.assertEqual(word_locations_in_samples, [
{'end_ts': 2240.0, 'start_ts': 0.0, 'word': ' Hi'},
{'end_ts': 4800.0, 'start_ts': 2240.0, 'word': ' there,'},
{'end_ts': 9280.0, 'start_ts': 7360.0, 'word': ' how'},
{'end_ts': 11200.0, 'start_ts': 9280.0, 'word': ' are'},
{'end_ts': 14080.0, 'start_ts': 11200.0, 'word': ' you?'}
])
def test_neural_asr_de_silero(self):
self.maxDiff = None
audio_transcript, word_locations_in_samples = helper_neural_asr("de", "silero")
assert audio_transcript == 'hallo wie geht es dir'
print("word_locations_in_samples:")
print(word_locations_in_samples)
self.assertEqual(word_locations_in_samples, [
{'word': 'hallo', 'start_ts': 0.0, 'end_ts': 6773.68},
{'word': 'wie', 'start_ts': 6773.68, 'end_ts': 10468.42},
{'word': 'geht', 'start_ts': 10468.42, 'end_ts': 13547.37},
{'word': 'es', 'start_ts': 13547.37, 'end_ts': 16626.32},
{'word': 'dir', 'start_ts': 16626.32, 'end_ts': 20321.05}
])
def test_neural_asr_en_silero(self):
self.maxDiff = None
audio_transcript, word_locations_in_samples = helper_neural_asr("en", "silero")
assert audio_transcript == 'i there how are you'
self.assertEqual(word_locations_in_samples, [
{'end_ts': 1800.0, 'start_ts': 0.0, 'word': 'i'},
{'end_ts': 5400.0, 'start_ts': 1800.0, 'word': 'there'},
{'end_ts': 8400.0, 'start_ts': 5400.0, 'word': 'how'},
{'end_ts': 12000.0, 'start_ts': 8400.0, 'word': 'are'},
{'end_ts': 15000.0, 'start_ts': 12000.0, 'word': 'you'}
])
if __name__ == '__main__':
unittest.main() |