| | |
|---|
| | | from funasr.models.frontend.wav_frontend import WavFrontend |
|---|
| | | from funasr.tasks.vad import VADTask |
|---|
| | | from funasr.bin.vad_inference import Speech2VadSegment |
|---|
| | | from funasr.utils.timestamp_tools import time_stamp_lfr6_pl |
|---|
| | | from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard |
|---|
| | | from funasr.bin.punctuation_infer import Text2Punc |
|---|
| | | from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer |
|---|
| | | |
|---|
| | | from funasr.utils.timestamp_tools import time_stamp_sentence |
|---|
| | | |
|---|
| | | header_colors = '\033[95m' |
|---|
| | | end_colors = '\033[0m' |
|---|
| | |
|---|
| | | |
|---|
| | | Examples: |
|---|
| | | >>> import soundfile |
|---|
| | | >>> speech2text = Speech2Text("asr_config.yml", "asr.pth") |
|---|
| | | >>> speech2text = Speech2Text("asr_config.yml", "asr.pb") |
|---|
| | | >>> audio, rate = soundfile.read("speech.wav") |
|---|
| | | >>> speech2text(audio) |
|---|
| | | [(text, token, token_int, hypothesis object), ...] |
|---|
| | |
|---|
| | | decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] |
|---|
| | | |
|---|
| | | if isinstance(self.asr_model, BiCifParaformer): |
|---|
| | | _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len, |
|---|
| | | _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len, |
|---|
| | | pre_token_length) # test no bias cif2 |
|---|
| | | |
|---|
| | | results = [] |
|---|
| | |
|---|
| | | |
|---|
| | | # remove blank symbol id, which is assumed to be 0 |
|---|
| | | token_int = list(filter(lambda x: x != 0 and x != 2, token_int)) |
|---|
| | | if len(token_int) == 0: |
|---|
| | | continue |
|---|
| | | |
|---|
| | | # Change integer-ids to tokens |
|---|
| | | token = self.converter.ids2tokens(token_int) |
|---|
| | |
|---|
| | | text = None |
|---|
| | | |
|---|
| | | if isinstance(self.asr_model, BiCifParaformer): |
|---|
| | | timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time) |
|---|
| | | _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], |
|---|
| | | us_peaks[i], |
|---|
| | | copy.copy(token), |
|---|
| | | vad_offset=begin_time) |
|---|
| | | results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor)) |
|---|
| | | else: |
|---|
| | | results.append((text, token, token_int, enc_len_batch_total, lfr_factor)) |
|---|
| | |
|---|
| | | ibest_writer["token"][key] = " ".join(token) |
|---|
| | | ibest_writer["token_int"][key] = " ".join(map(str, token_int)) |
|---|
| | | ibest_writer["vad"][key] = "{}".format(vadsegments) |
|---|
| | | ibest_writer["text"][key] = text_postprocessed |
|---|
| | | ibest_writer["text"][key] = " ".join(word_lists) |
|---|
| | | ibest_writer["text_with_punc"][key] = text_postprocessed_punc |
|---|
| | | if time_stamp_postprocessed is not None: |
|---|
| | | ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed) |
|---|
