From 2cc6b504537fd4f54a7063f0b102917c61f05456 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 14 四月 2023 15:52:43 +0800
Subject: [PATCH] Merge pull request #352 from alibaba-damo-academy/dev_lhn2
---
funasr/bin/asr_inference_paraformer_streaming.py | 17 +++++++++++------
funasr/models/predictor/cif.py | 12 +++++++++---
2 files changed, 20 insertions(+), 9 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer_streaming.py b/funasr/bin/asr_inference_paraformer_streaming.py
index 66dec39..3520297 100644
--- a/funasr/bin/asr_inference_paraformer_streaming.py
+++ b/funasr/bin/asr_inference_paraformer_streaming.py
@@ -19,6 +19,7 @@
import numpy as np
import torch
+import torchaudio
from typeguard import check_argument_types
from funasr.fileio.datadir_writer import DatadirWriter
@@ -607,17 +608,21 @@
):
# 3. Build data-iterator
- if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
- raw_inputs = _load_bytes(data_path_and_name_and_type[0])
- raw_inputs = torch.tensor(raw_inputs)
- if data_path_and_name_and_type is None and raw_inputs is not None:
- if isinstance(raw_inputs, np.ndarray):
- raw_inputs = torch.tensor(raw_inputs)
is_final = False
if param_dict is not None and "cache" in param_dict:
cache = param_dict["cache"]
if param_dict is not None and "is_final" in param_dict:
is_final = param_dict["is_final"]
+
+ if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
+ raw_inputs = _load_bytes(data_path_and_name_and_type[0])
+ raw_inputs = torch.tensor(raw_inputs)
+ if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
+ raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
+ is_final = True
+ if data_path_and_name_and_type is None and raw_inputs is not None:
+ if isinstance(raw_inputs, np.ndarray):
+ raw_inputs = torch.tensor(raw_inputs)
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
asr_result_list = []
diff --git a/funasr/models/predictor/cif.py b/funasr/models/predictor/cif.py
index e80a915..a5273f8 100644
--- a/funasr/models/predictor/cif.py
+++ b/funasr/models/predictor/cif.py
@@ -234,6 +234,7 @@
last_fire_place = len_time - 1
last_fire_remainds = 0.0
pre_alphas_length = 0
+ last_fire = False
mask_chunk_peak_predictor = None
if cache is not None:
@@ -251,10 +252,15 @@
if cif_peak[0][len_time - 1 - i] > self.threshold or cif_peak[0][len_time - 1 - i] == self.threshold:
last_fire_place = len_time - 1 - i
last_fire_remainds = cif_peak[0][len_time - 1 - i] - self.threshold
+ last_fire = True
break
- last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
- cache["cif_hidden"] = hidden[:, last_fire_place:, :]
- cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
+ if last_fire:
+ last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
+ cache["cif_hidden"] = hidden[:, last_fire_place:, :]
+ cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
+ else:
+ cache["cif_hidden"] = hidden
+ cache["cif_alphas"] = alphas
token_num_int = token_num.floor().type(torch.int32).item()
return acoustic_embeds[:, 0:token_num_int, :], token_num, alphas, cif_peak
--
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