def test_online_decoder_decoding_with_two_calls_no_lm(self):
decoder = ctcdecode.OnlineCTCBeamDecoder(
self.vocab_list,
beam_width=self.beam_size,
blank_id=self.vocab_list.index("_"),
log_probs_input=True,
num_processes=24,
)
state1 = ctcdecode.DecoderState(decoder)
state2 = ctcdecode.DecoderState(decoder)
probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]).log()
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq[:, :2], [state1, state2], [False, False])
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq[:, 2:], [state1, state2], [True, True])
del state1, state2
size = beam_results.shape
output_str1 = self.convert_to_string(beam_results[0][0],
self.vocab_list,
out_seq_len[0][0])
output_str2 = self.convert_to_string(beam_results[1][0],
self.vocab_list,
out_seq_len[1][0])
self.assertEqual(output_str1, self.beam_search_result[0])
self.assertEqual(output_str2, self.beam_search_result[1])
def test_online_decoder_decoding_no_lm(self):
decoder = ctcdecode.OnlineCTCBeamDecoder(
self.vocab_list,
beam_width=self.beam_size,
blank_id=self.vocab_list.index("_"),
log_probs_input=True,
num_processes=24,
)
state1 = ctcdecode.DecoderState(decoder)
state2 = ctcdecode.DecoderState(decoder)
probs_seq = torch.FloatTensor([self.probs_seq1, self.probs_seq2]).log()
is_eos_s = [True for _ in range(len(probs_seq))]
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq, [state1, state2], is_eos_s)
output_str1 = self.convert_to_string(beam_results[0][0],
self.vocab_list,
out_seq_len[0][0])
output_str2 = self.convert_to_string(beam_results[1][0],
self.vocab_list,
out_seq_len[1][0])
self.assertEqual(output_str1, self.beam_search_result[0])
self.assertEqual(output_str2, self.beam_search_result[1])
def test_online_decoder_decoding_with_a_lot_calls_no_lm_check_size(self):
decoder = ctcdecode.OnlineCTCBeamDecoder(
self.vocab_list,
beam_width=self.beam_size,
blank_id=self.vocab_list.index("_"),
log_probs_input=True,
num_processes=24,
)
state1 = ctcdecode.DecoderState(decoder)
probs_seq = torch.FloatTensor([self.probs_seq1]).log()
for i in range(1000):
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq, [state1], [False, False])
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq, [state1], [True, True])
del state1
self.assertGreaterEqual(beam_results.shape[2], out_seq_len.max())
def test_online_decoder_decoding_with_two_calls(self):
lm_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"test.arpa")
decoder = ctcdecode.OnlineCTCBeamDecoder(
self.vocab_list,
beam_width=self.beam_size,
blank_id=self.vocab_list.index("_"),
log_probs_input=True,
num_processes=24,
model_path=lm_path,
)
state1 = ctcdecode.DecoderState(decoder)
probs_seq = torch.FloatTensor([self.probs_seq2]).log()
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq[:, :2], [state1], [False])
beam_results, beam_scores, timesteps, out_seq_len = decoder.decode(
probs_seq[:, 2:], [state1], [True])
output_str1 = self.convert_to_string(beam_results[0][0],
self.vocab_list,
out_seq_len[0][0])
self.assertEqual(output_str1, self.beam_search_result[2])