From 00c1d9119c8154eda88cee4ad0719a99de795f17 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 03 三月 2023 21:53:20 +0800
Subject: [PATCH] Merge pull request #184 from alibaba-damo-academy/dev_timestamp
---
funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py | 55 +++++++++++++++++++++++++++++++++++++++++--------------
1 files changed, 41 insertions(+), 14 deletions(-)
diff --git a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
index e6b33d4..3545ccf 100644
--- a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
+++ b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
@@ -22,6 +22,8 @@
def __init__(self, model_dir: Union[str, Path] = None,
batch_size: int = 1,
device_id: Union[str, int] = "-1",
+ plot_timestamp_to: str = "",
+ pred_bias: int = 1,
):
if not Path(model_dir).exists():
@@ -40,17 +42,17 @@
)
self.ort_infer = torch.jit.load(model_file)
self.batch_size = batch_size
+ self.plot_timestamp_to = plot_timestamp_to
+ self.pred_bias = pred_bias
def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
waveform_nums = len(waveform_list)
-
asr_res = []
for beg_idx in range(0, waveform_nums, self.batch_size):
- res = {}
+
end_idx = min(waveform_nums, beg_idx + self.batch_size)
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
-
try:
outputs = self.ort_infer(feats, feats_len)
am_scores, valid_token_lens = outputs[0], outputs[1]
@@ -65,14 +67,41 @@
preds = ['']
else:
am_scores, valid_token_lens = am_scores.detach().cpu().numpy(), valid_token_lens.detach().cpu().numpy()
- preds, raw_token = self.decode(am_scores, valid_token_lens)[0]
- res['preds'] = preds
- if us_cif_peak is not None:
- us_alphas, us_cif_peak = us_alphas.cpu().numpy(), us_cif_peak.cpu().numpy()
- timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(raw_token), log=False)
- res['timestamp'] = timestamp
- asr_res.append(res)
+ preds = self.decode(am_scores, valid_token_lens)
+ if us_cif_peak is None:
+ for pred in preds:
+ 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))
+ 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})
return asr_res
+
+ def plot_wave_timestamp(self, wav, text_timestamp, dest):
+ # TODO: Plot the wav and timestamp results with matplotlib
+ import matplotlib
+ matplotlib.use('Agg')
+ matplotlib.rc("font", family='Alibaba PuHuiTi') # set it to a font that your system supports
+ import matplotlib.pyplot as plt
+ fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320)
+ ax2 = ax1.twinx()
+ ax2.set_ylim([0, 2.0])
+ # plot waveform
+ ax1.set_ylim([-0.3, 0.3])
+ time = np.arange(wav.shape[0]) / 16000
+ ax1.plot(time, wav/wav.max()*0.3, color='gray', alpha=0.4)
+ # plot lines and text
+ for (char, start, end) in text_timestamp:
+ ax1.vlines(start, -0.3, 0.3, ls='--')
+ ax1.vlines(end, -0.3, 0.3, ls='--')
+ x_adj = 0.045 if char != '<sil>' else 0.12
+ ax1.text((start + end) * 0.5 - x_adj, 0, char)
+ # plt.legend()
+ plotname = "{}/timestamp.png".format(dest)
+ plt.savefig(plotname, bbox_inches='tight')
def load_data(self,
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -148,9 +177,7 @@
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
- # token = token[:valid_token_num-1]
+ token = token[:valid_token_num-self.pred_bias]
texts = sentence_postprocess(token)
- text = texts[0]
- # text = self.tokenizer.tokens2text(token)
- return text, token
+ return texts
--
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