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support wav_file input

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This commit is contained in:
haoneng.lhn 2023-04-14 11:47:28 +08:00
parent f0fdc051fb
commit 5589b4a617
2 changed files with 20 additions and 9 deletions
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17
funasr/bin/asr_inference_paraformer_streaming.py
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@ -19,6 +19,7 @@ from typing import List
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 @@ def inference_modelscope(
):
# 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 = []

12
funasr/models/predictor/cif.py
View File

@ -234,6 +234,7 @@ class CifPredictorV2(nn.Module):
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 @@ class CifPredictorV2(nn.Module):
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