def test_decoded_vs_generated():
pipeline = DecoderPipeline()
pipeline.build()
idx = 0
for iter in range(1):
out = pipeline.run()
for i in range(len(out[0])):
plain = out[0].at(i)
res = out[1].at(i)
mix = out[2].at(i)[:, np.newaxis]
res_mix = out[3].at(i)[:, np.newaxis]
ref_len = [0, 0, 0, 0]
ref_len[0] = lengths[idx]
ref_len[1] = lengths[idx] * rate1 / rates[idx]
ref_len[2] = lengths[idx]
ref_len[3] = lengths[idx] * rate2 / rates[idx]
ref0 = generate_waveforms(ref_len[0], freqs[idx]) * 32767
ref1 = generate_waveforms(ref_len[1], freqs[idx] *
(rates[idx] / rate1)) * 32767
ref2 = generate_waveforms(ref_len[2], freqs[idx]) * 32767
ref2 = ref2.mean(axis=1, keepdims=1)
ref3 = generate_waveforms(ref_len[3],
freqs[idx] * (rates[idx] / rate2))
ref3 = ref3.mean(axis=1, keepdims=1)
assert out[4].at(i) == rates[idx]
assert out[5].at(i) == rate1
assert out[6].at(i) == rates[idx]
assert out[7].at(i) == rate2
# just reading - allow only for rounding
assert np.allclose(plain, ref0, rtol=0, atol=0.5)
# resampling - allow for 1e-3 dynamic range error
assert np.allclose(res, ref1, rtol=0, atol=32767 * 1e-3)
# downmixing - allow for 2 bits of error
# - one for quantization of channels, one for quantization of result
assert np.allclose(mix, ref2, rtol=0, atol=2)
# resampling with weird ratio - allow for 3e-3 dynamic range error
assert np.allclose(res_mix, ref3, rtol=0, atol=3e-3)
rosa_in1 = plain.astype(np.float32)
rosa1 = rosa_resample(rosa_in1, rates[idx], rate1)
rosa_in3 = rosa_in1 / 32767
rosa3 = rosa_resample(rosa_in3.mean(axis=1, keepdims=1),
rates[idx], rate2)
assert np.allclose(res, rosa1, rtol=0, atol=32767 * 1e-3)
assert np.allclose(res_mix, rosa3, rtol=0, atol=3e-3)
idx = (idx + 1) % len(names)
def create_ref():
ref = []
for i in range(len(names)):
wave = generate_waveforms(lengths[i], freqs[i])
wave = (wave * 32767).round().astype(np.int16)
ref.append(wave)
return ref
def create_files():
for i in range(len(names)):
wave = generate_waveforms(lengths[i], freqs[i])
wave = (wave * 32767).round().astype(np.int16)
scipy.io.wavfile.write(names[i], rates[i], wave)
def test_decoded_vs_generated(batch_size=3):
pipeline = NemoAsrReaderPipeline(batch_size=batch_size)
pipeline.build()
for iter in range(1):
out = pipeline.run()
for idx in range(batch_size):
audio_plain_i = out[0].at(idx)
audio_plain_f = out[1].at(idx)
audio_downmix_i = out[2].at(idx)
audio_downmix_f = out[3].at(idx)
audio_resampled1_i = out[4].at(idx)
audio_resampled1_f = out[5].at(idx)
audio_resampled2_i = out[6].at(idx)
audio_resampled2_f = out[7].at(idx)
text = out[8].at(idx)
text_non_ascii = out[9].at(idx)
ref_plain_i = ref_i[idx]
np.testing.assert_allclose(audio_plain_i, ref_plain_i, rtol=1e-7)
ref_plain_f = ref_i[idx].astype(np.float32) / 32767
np.testing.assert_allclose(audio_plain_f, ref_plain_f, rtol=1e-4)
ref_downmix_i_float = ref_i[idx].astype(np.float32).mean(
axis=1, keepdims=1)
ref_downmix_i = ref_downmix_i_float.astype(np.int16).flatten()
np.testing.assert_allclose(audio_downmix_i, ref_downmix_i, atol=1)
ref_downmix_f = (ref_downmix_i_float / 32767).flatten()
np.testing.assert_allclose(audio_downmix_f,
ref_downmix_f,
rtol=1e-4)
ref_resampled1_float = generate_waveforms(
lengths[idx] * rate1 / rates[idx],
freqs[idx] * (rates[idx] / rate1))
ref_resampled1_downmix = ref_resampled1_float.astype(
np.float32).mean(axis=1, keepdims=1)
ref_resampled1_i = (ref_resampled1_downmix * 32767).astype(
np.int16).flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled1_i,
ref_resampled1_i,
atol=round(32767 * 1e-3))
ref_resampled1_f = ref_resampled1_downmix.flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled1_f,
ref_resampled1_f,
atol=1e-3)
ref_resampled2_float = generate_waveforms(
lengths[idx] * rate2 / rates[idx],
freqs[idx] * (rates[idx] / rate2))
ref_resampled2_downmix = ref_resampled2_float.astype(
np.float32).mean(axis=1, keepdims=1)
ref_resampled2_i = (ref_resampled2_downmix * 32767).astype(
np.int16).flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled2_i,
ref_resampled2_i,
atol=round(32767 * 1e-3))
ref_resampled2_f = ref_resampled2_downmix.flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled2_f,
ref_resampled2_f,
atol=1e-3)
np.testing.assert_equal(text, ref_text[idx])
np.testing.assert_equal(text_non_ascii, ref_text_non_ascii[idx])
text_non_ascii_str = str(text_non_ascii.tobytes(), encoding='utf8')
# Checking that we don't have any trailing zeros
# (those won't be caught by the string comparison)
ref_text_non_ascii_literal_bytes = bytes(
ref_text_non_ascii_literal[idx], 'utf8')
assert text_non_ascii.tobytes() == ref_text_non_ascii_literal_bytes, \
f"'{text_non_ascii.tobytes()}' != '{ref_text_non_ascii_literal_bytes}'"
# String comparison (utf-8)
assert text_non_ascii_str == ref_text_non_ascii_literal[idx], \
f"'{text_non_ascii_str}' != '{ref_text_non_ascii_literal[idx]}'"
def test_decoded_vs_generated(batch_size=3):
pipeline = NemoAsrReaderPipeline(batch_size=batch_size)
pipeline.build()
for iter in range(1):
out = pipeline.run()
for idx in range(batch_size):
audio_plain_i = out[0].at(idx)
audio_plain_f = out[1].at(idx)
audio_downmix_i = out[2].at(idx)
audio_downmix_f = out[3].at(idx)
audio_resampled1_i = out[4].at(idx)
audio_resampled1_f = out[5].at(idx)
audio_resampled2_i = out[6].at(idx)
audio_resampled2_f = out[7].at(idx)
text = out[8].at(idx)
ref_plain_i = ref_i[idx]
np.testing.assert_allclose(audio_plain_i, ref_plain_i, rtol=1e-7)
ref_plain_f = ref_i[idx].astype(np.float32) / 32767
np.testing.assert_allclose(audio_plain_f, ref_plain_f, rtol=1e-4)
ref_downmix_i_float = ref_i[idx].astype(np.float32).mean(
axis=1, keepdims=1)
ref_downmix_i = ref_downmix_i_float.astype(np.int16).flatten()
np.testing.assert_allclose(audio_downmix_i, ref_downmix_i, atol=1)
ref_downmix_f = (ref_downmix_i_float / 32767).flatten()
np.testing.assert_allclose(audio_downmix_f,
ref_downmix_f,
rtol=1e-4)
ref_resampled1_float = generate_waveforms(
lengths[idx] * rate1 / rates[idx],
freqs[idx] * (rates[idx] / rate1))
ref_resampled1_downmix = ref_resampled1_float.astype(
np.float32).mean(axis=1, keepdims=1)
ref_resampled1_i = (ref_resampled1_downmix * 32767).astype(
np.int16).flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled1_i,
ref_resampled1_i,
atol=round(32767 * 1e-3))
ref_resampled1_f = ref_resampled1_downmix.flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled1_f,
ref_resampled1_f,
atol=1e-3)
ref_resampled2_float = generate_waveforms(
lengths[idx] * rate2 / rates[idx],
freqs[idx] * (rates[idx] / rate2))
ref_resampled2_downmix = ref_resampled2_float.astype(
np.float32).mean(axis=1, keepdims=1)
ref_resampled2_i = (ref_resampled2_downmix * 32767).astype(
np.int16).flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled2_i,
ref_resampled2_i,
atol=round(32767 * 1e-3))
ref_resampled2_f = ref_resampled2_downmix.flatten()
# resampling - allow for 1e-3 dynamic range error
np.testing.assert_allclose(audio_resampled2_f,
ref_resampled2_f,
atol=1e-3)
np.testing.assert_equal(text, ref_text[idx])
