From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
examples/industrial_data_pretraining/seaco_paraformer/demo.py | 20 +++++++++++---------
1 files changed, 11 insertions(+), 9 deletions(-)
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/demo.py b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
index e9e226d..c7f78d3 100644
--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
@@ -6,25 +6,27 @@
from funasr import AutoModel
model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
- model_revision="v2.0.4",
- vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.4",
- punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
- punc_model_revision="v2.0.4",
- # spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
- # spk_model_revision="v2.0.2",
+ # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+ # punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+ # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
)
# example1
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
hotword='杈炬懇闄� 榄旀惌',
+ # return_raw_text=True, # return raw text recognition results splited by space of equal length with timestamp
+ # preset_spk_num=2, # preset speaker num for speaker cluster model
# sentence_timestamp=True, # return sentence level information when spk_model is not given
)
print(res)
+
+'''
+# tensor or numpy as input
# example2
import torchaudio
+import os
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
input_tensor, sample_rate = torchaudio.load(wav_file)
input_tensor = input_tensor.mean(0)
@@ -33,8 +35,8 @@
# example3
import soundfile
-import os
+
wav_file = os.path.join(model.model_path, "example/asr_example.wav")
speech, sample_rate = soundfile.read(wav_file)
res = model.generate(input=[speech], batch_size_s=300, is_final=True)
-
+'''
--
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