diff --git a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py index d47135aa0..3c0606d6c 100644 --- a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py +++ b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py @@ -62,26 +62,28 @@ class Paraformer(): am_scores, valid_token_lens = outputs[0], outputs[1] if len(outputs) == 4: # for BiCifParaformer Inference - us_alphas, us_cif_peak = outputs[2], outputs[3] + us_alphas, us_peaks = outputs[2], outputs[3] else: - us_alphas, us_cif_peak = None, None + us_alphas, us_peaks = None, None except: #logging.warning(traceback.format_exc()) logging.warning("input wav is silence or noise") preds = [''] else: - am_scores, valid_token_lens = am_scores.detach().cpu().numpy(), valid_token_lens.detach().cpu().numpy() preds = self.decode(am_scores, valid_token_lens) - if us_cif_peak is None: + if us_peaks is None: for pred in preds: + pred = sentence_postprocess(pred) asr_res.append({'preds': pred}) else: - for pred, us_cif_peak_ in zip(preds, us_cif_peak): - text, tokens = pred - timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens)) + for pred, us_peaks_ in zip(preds, us_peaks): + raw_tokens = pred + timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens)) + text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw) + # logging.warning(timestamp) if len(self.plot_timestamp_to): - self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to) - asr_res.append({'preds': text, 'timestamp': timestamp}) + self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to) + asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens}) return asr_res def plot_wave_timestamp(self, wav, text_timestamp, dest): @@ -182,6 +184,6 @@ class Paraformer(): # Change integer-ids to tokens token = self.converter.ids2tokens(token_int) token = token[:valid_token_num-self.pred_bias] - texts = sentence_postprocess(token) - return texts + # texts = sentence_postprocess(token) + return token diff --git a/funasr/runtime/python/libtorch/torch_paraformer/utils/timestamp_utils.py b/funasr/runtime/python/libtorch/torch_paraformer/utils/timestamp_utils.py index 767e864fc..3a01812e8 100644 --- a/funasr/runtime/python/libtorch/torch_paraformer/utils/timestamp_utils.py +++ b/funasr/runtime/python/libtorch/torch_paraformer/utils/timestamp_utils.py @@ -1,11 +1,11 @@ import numpy as np -def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0): +def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5): if not len(char_list): return [] START_END_THRESHOLD = 5 - MAX_TOKEN_DURATION = 14 + MAX_TOKEN_DURATION = 30 TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled cif_peak = us_cif_peak.reshape(-1) num_frames = cif_peak.shape[-1] @@ -16,7 +16,7 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0): new_char_list = [] # for bicif model trained with large data, cif2 actually fires when a character starts # so treat the frames between two peaks as the duration of the former token - fire_place = np.where(cif_peak>1.0-1e-4)[0] - 1.5 # np format + fire_place = np.where(cif_peak>1.0-1e-4)[0] + total_offset # np format num_peak = len(fire_place) assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1 # begin silence @@ -27,7 +27,7 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0): # tokens timestamp for i in range(len(fire_place)-1): new_char_list.append(char_list[i]) - if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION: + if i == len(fire_place)-2 or MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION: timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE]) else: # cut the duration to token and sil of the 0-weight frames last long @@ -48,11 +48,12 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0): timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0 timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0 assert len(new_char_list) == len(timestamp_list) - res_total = [] + res_str = "" for char, timestamp in zip(new_char_list, timestamp_list): - res_total.append([char, timestamp[0], timestamp[1]]) # += "{} {} {};".format(char, timestamp[0], timestamp[1]) + res_str += "{} {} {};".format(char, timestamp[0], timestamp[1]) res = [] for char, timestamp in zip(new_char_list, timestamp_list): if char != '': res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)]) - return res, res_total \ No newline at end of file + return res_str, res + \ No newline at end of file diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py index 61c85ec4c..556794087 100644 --- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py +++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py @@ -64,25 +64,28 @@ class Paraformer(): am_scores, valid_token_lens = outputs[0], outputs[1] if len(outputs) == 4: # for BiCifParaformer Inference - us_alphas, us_cif_peak = outputs[2], outputs[3] + us_alphas, us_peaks = outputs[2], outputs[3] else: - us_alphas, us_cif_peak = None, None + us_alphas, us_peaks = None, None except ONNXRuntimeError: #logging.warning(traceback.format_exc()) logging.warning("input wav is silence or noise") preds = [''] else: preds = self.decode(am_scores, valid_token_lens) - if us_cif_peak is None: + if us_peaks is None: for pred in preds: + pred = sentence_postprocess(pred) asr_res.append({'preds': pred}) else: - for pred, us_cif_peak_ in zip(preds, us_cif_peak): - text, tokens = pred - timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens)) + for pred, us_peaks_ in zip(preds, us_peaks): + raw_tokens = pred + timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens)) + text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw) + # logging.warning(timestamp) if len(self.plot_timestamp_to): - self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to) - asr_res.append({'preds': text, 'timestamp': timestamp}) + self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to) + asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens}) return asr_res def plot_wave_timestamp(self, wav, text_timestamp, dest): @@ -181,6 +184,6 @@ class Paraformer(): # Change integer-ids to tokens token = self.converter.ids2tokens(token_int) token = token[:valid_token_num-self.pred_bias] - texts = sentence_postprocess(token) - return texts + # texts = sentence_postprocess(token) + return token diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py index dd702f39f..3a01812e8 100644 --- a/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py +++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py @@ -48,12 +48,12 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1 timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0 timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0 assert len(new_char_list) == len(timestamp_list) - res_total = [] + res_str = "" for char, timestamp in zip(new_char_list, timestamp_list): - res_total.append([char, timestamp[0], timestamp[1]]) # += "{} {} {};".format(char, timestamp[0], timestamp[1]) + res_str += "{} {} {};".format(char, timestamp[0], timestamp[1]) res = [] for char, timestamp in zip(new_char_list, timestamp_list): if char != '': res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)]) - return res, res_total + return res_str, res \ No newline at end of file