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Begin to support CTC models (#119)

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Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.
This commit is contained in:
Fangjun Kuang
2023-04-07 23:11:34 +08:00
committed by GitHub
parent 9ac747248b
commit 80060c276d
40 changed files with 1244 additions and 60 deletions
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65
sherpa-onnx/python/tests/test_offline_recognizer.py
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@@ -196,6 +196,71 @@ class TestOfflineRecognizer(unittest.TestCase):
print(s2.result.text)
print(s3.result.text)
def test_nemo_ctc_single_file(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.onnx"
tokens = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/tokens.txt"
wave0 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav"
if not Path(model).is_file():
print("skipping test_nemo_ctc_single_file()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_ctc(
model=model,
tokens=tokens,
num_threads=1,
)
s = recognizer.create_stream()
samples, sample_rate = read_wave(wave0)
s.accept_waveform(sample_rate, samples)
recognizer.decode_stream(s)
print(s.result.text)
def test_nemo_ctc_multiple_files(self):
for use_int8 in [True, False]:
if use_int8:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.int8.onnx"
else:
model = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/model.onnx"
tokens = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/tokens.txt"
wave0 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/0.wav"
wave1 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/1.wav"
wave2 = f"{d}/sherpa-onnx-nemo-ctc-en-citrinet-512/test_wavs/8k.wav"
if not Path(model).is_file():
print("skipping test_nemo_ctc_multiple_files()")
return
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_ctc(
model=model,
tokens=tokens,
num_threads=1,
)
s0 = recognizer.create_stream()
samples0, sample_rate0 = read_wave(wave0)
s0.accept_waveform(sample_rate0, samples0)
s1 = recognizer.create_stream()
samples1, sample_rate1 = read_wave(wave1)
s1.accept_waveform(sample_rate1, samples1)
s2 = recognizer.create_stream()
samples2, sample_rate2 = read_wave(wave2)
s2.accept_waveform(sample_rate2, samples2)
recognizer.decode_streams([s0, s1, s2])
print(s0.result.text)
print(s1.result.text)
print(s2.result.text)
if __name__ == "__main__":
unittest.main()