From eba1fccfa07665ed9cada19cfab6cd342e03112e Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期二, 16 一月 2024 11:34:04 +0800
Subject: [PATCH] Merge pull request #1250 from alibaba-damo-academy/funasr1.0
---
examples/industrial_data_pretraining/ct_transformer_streaming/demo.py | 2
examples/industrial_data_pretraining/ct_transformer/demo.py | 4
examples/industrial_data_pretraining/paraformer-zh-spk/demo.py | 4
funasr/auto/auto_model.py | 416 +++++++++++++++++++++
examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py | 4
funasr/auto/auto_tokenizer.py | 8
examples/industrial_data_pretraining/contextual_paraformer/demo.py | 2
examples/industrial_data_pretraining/monotonic_aligner/demo.py | 2
README_zh.md | 18
examples/industrial_data_pretraining/campplus_sv/demo.py | 2
README.md | 24
funasr/bin/inference.py | 474 ------------------------
examples/industrial_data_pretraining/emotion2vec/demo.py | 2
examples/industrial_data_pretraining/paraformer/demo.py | 4
funasr/auto/auto_frontend.py | 95 +++++
examples/industrial_data_pretraining/bicif_paraformer/demo.py | 16
funasr/auto/__init__.py | 0
examples/industrial_data_pretraining/seaco_paraformer/demo.py | 4
funasr/__init__.py | 3
examples/industrial_data_pretraining/paraformer_streaming/demo.py | 16
20 files changed, 581 insertions(+), 519 deletions(-)
diff --git a/README.md b/README.md
index a53ce4d..2bd28e2 100644
--- a/README.md
+++ b/README.md
@@ -95,9 +95,9 @@
vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
spk_model="cam++", spk_model_revision="v2.0.2")
-res = model(input=f"{model.model_path}/example/asr_example.wav",
- batch_size=64,
- hotword='榄旀惌')
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
+ batch_size=64,
+ hotword='榄旀惌')
print(res)
```
Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
@@ -124,7 +124,7 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
@@ -135,7 +135,7 @@
model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav"
-res = model(input=wav_file)
+res = model.generate(input=wav_file)
print(res)
```
### Voice Activity Detection (Non-streaming)
@@ -156,7 +156,7 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
@@ -165,7 +165,7 @@
from funasr import AutoModel
model = AutoModel(model="ct-punc", model_revision="v2.0.2")
-res = model(input="閭ｄ粖澶╃殑浼氬氨鍒拌繖閲屽惂 happy new year 鏄庡勾瑙�")
+res = model.generate(input="閭ｄ粖澶╃殑浼氬氨鍒拌繖閲屽惂 happy new year 鏄庡勾瑙�")
print(res)
```
### Timestamp Prediction
@@ -175,7 +175,7 @@
model = AutoModel(model="fa-zh", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
-res = model(input=(wav_file, text_file), data_type=("sound", "text"))
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)
@@ -229,10 +229,16 @@
}
@inproceedings{gao22b_interspeech,
author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
- title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
+ title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={2063--2067},
doi={10.21437/Interspeech.2022-9996}
}
+@inproceedings{shi2023seaco,
+ author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
+ title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
+ year={2023},
+ booktitle={ICASSP2024}
+}
```
diff --git a/README_zh.md b/README_zh.md
index 861e61c..dc20302 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -91,7 +91,7 @@
vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
spk_model="cam++", spk_model_revision="v2.0.2")
-res = model(input=f"{model.model_path}/example/asr_example.wav",
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size=64,
hotword='榄旀惌')
print(res)
@@ -121,7 +121,7 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
@@ -134,7 +134,7 @@
model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
wav_file = f"{model.model_path}/example/asr_example.wav"
-res = model(input=wav_file)
+res = model.generate(input=wav_file)
print(res)
```
@@ -156,7 +156,7 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
@@ -167,7 +167,7 @@
model = AutoModel(model="ct-punc", model_revision="v2.0.2")
-res = model(input="閭ｄ粖澶╃殑浼氬氨鍒拌繖閲屽惂 happy new year 鏄庡勾瑙�")
+res = model.generate(input="閭ｄ粖澶╃殑浼氬氨鍒拌繖閲屽惂 happy new year 鏄庡勾瑙�")
print(res)
```
@@ -179,7 +179,7 @@
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
-res = model(input=(wav_file, text_file), data_type=("sound", "text"))
+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
鏇村璇︾粏鐢ㄦ硶锛圼绀轰緥](examples/industrial_data_pretraining)锛�
@@ -242,4 +242,10 @@
pages={2063--2067},
doi={10.21437/Interspeech.2022-9996}
}
+@article{shi2023seaco,
+ author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
+ title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}},
+ year=2023,
+ journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)},
+}
```
diff --git a/examples/industrial_data_pretraining/bicif_paraformer/demo.py b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
index 60718de..a06b308 100644
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
@@ -6,14 +6,14 @@
from funasr import AutoModel
model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
- model_revision="v2.0.2",
- vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.2",
- punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
- punc_model_revision="v2.0.2",
- spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
- spk_model_revision="v2.0.2",
+ model_revision="v2.0.2",
+ vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+ vad_model_revision="v2.0.2",
+ punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
+ punc_model_revision="v2.0.2",
+ spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
+ spk_model_revision="v2.0.2",
)
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
print(res)
diff --git a/examples/industrial_data_pretraining/campplus_sv/demo.py b/examples/industrial_data_pretraining/campplus_sv/demo.py
index 6a7f105..16d629b 100644
--- a/examples/industrial_data_pretraining/campplus_sv/demo.py
+++ b/examples/industrial_data_pretraining/campplus_sv/demo.py
@@ -9,5 +9,5 @@
model_revision="v2.0.2",
)
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/contextual_paraformer/demo.py b/examples/industrial_data_pretraining/contextual_paraformer/demo.py
index 78693c5..d1378ca 100644
--- a/examples/industrial_data_pretraining/contextual_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/contextual_paraformer/demo.py
@@ -7,6 +7,6 @@
model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.2")
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+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')
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/ct_transformer/demo.py b/examples/industrial_data_pretraining/ct_transformer/demo.py
index d648f3d..f547f03 100644
--- a/examples/industrial_data_pretraining/ct_transformer/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer/demo.py
@@ -7,7 +7,7 @@
model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.2")
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res)
@@ -15,5 +15,5 @@
model = AutoModel(model="damo/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.2")
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
index 5ef8381..081fd19 100644
--- a/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/ct_transformer_streaming/demo.py
@@ -12,7 +12,7 @@
rec_result_all = "outputs: "
cache = {}
for vad in vads:
- rec_result = model(input=vad, cache=cache)
+ rec_result = model.generate(input=vad, cache=cache)
print(rec_result)
rec_result_all += rec_result[0]['text']
diff --git a/examples/industrial_data_pretraining/emotion2vec/demo.py b/examples/industrial_data_pretraining/emotion2vec/demo.py
index abaa9f4..ea8da99 100644
--- a/examples/industrial_data_pretraining/emotion2vec/demo.py
+++ b/examples/industrial_data_pretraining/emotion2vec/demo.py
@@ -7,5 +7,5 @@
model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.1")
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
index 459dfff..8084dec 100644
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
@@ -9,7 +9,7 @@
chunk_size = 60000 # ms
model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2")
-res = model(input=wav_file, chunk_size=chunk_size, )
+res = model.generate(input=wav_file, chunk_size=chunk_size, )
print(res)
@@ -28,7 +28,7 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk,
+ res = model.generate(input=speech_chunk,
cache=cache,
is_final=is_final,
chunk_size=chunk_size,
diff --git a/examples/industrial_data_pretraining/monotonic_aligner/demo.py b/examples/industrial_data_pretraining/monotonic_aligner/demo.py
index def6b7d..cad9aab 100644
--- a/examples/industrial_data_pretraining/monotonic_aligner/demo.py
+++ b/examples/industrial_data_pretraining/monotonic_aligner/demo.py
@@ -7,7 +7,7 @@
model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.2")
-res = model(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
"娆㈣繋澶у鏉ュ埌榄旀惌绀惧尯杩涜浣撻獙"),
data_type=("sound", "text"),
batch_size=2,
diff --git a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
index aa895eb..b4453e9 100644
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
@@ -15,6 +15,6 @@
spk_model_revision="v2.0.2"
)
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
- hotword='杈炬懇闄� 纾ㄦ惌')
+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� 纾ㄦ惌')
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer/demo.py b/examples/industrial_data_pretraining/paraformer/demo.py
index 6dbe33d..ef33bf4 100644
--- a/examples/industrial_data_pretraining/paraformer/demo.py
+++ b/examples/industrial_data_pretraining/paraformer/demo.py
@@ -7,7 +7,7 @@
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.2")
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res)
@@ -18,5 +18,5 @@
fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)
for batch_idx, fbank_dict in enumerate(fbanks):
- res = model(**fbank_dict)
+ res = model.generate(**fbank_dict)
print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/paraformer_streaming/demo.py b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
index 8f7eef3..07efde6 100644
--- a/examples/industrial_data_pretraining/paraformer_streaming/demo.py
+++ b/examples/industrial_data_pretraining/paraformer_streaming/demo.py
@@ -11,7 +11,7 @@
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.2")
cache = {}
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
+res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
chunk_size=chunk_size,
encoder_chunk_look_back=encoder_chunk_look_back,
decoder_chunk_look_back=decoder_chunk_look_back,
@@ -32,11 +32,11 @@
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
- res = model(input=speech_chunk,
- cache=cache,
- is_final=is_final,
- chunk_size=chunk_size,
- encoder_chunk_look_back=encoder_chunk_look_back,
- decoder_chunk_look_back=decoder_chunk_look_back,
- )
+ res = model.generate(input=speech_chunk,
+ cache=cache,
+ is_final=is_final,
+ chunk_size=chunk_size,
+ encoder_chunk_look_back=encoder_chunk_look_back,
+ decoder_chunk_look_back=decoder_chunk_look_back,
+ )
print(res)
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/demo.py b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
index cf49e42..5f17252 100644
--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
@@ -15,6 +15,6 @@
spk_model_revision="v2.0.2",
)
-res = model(input=f"{model.model_path}/example/asr_example.wav",
- hotword='杈炬懇闄� 榄旀惌')
+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
+ hotword='杈炬懇闄� 榄旀惌')
print(res)
\ No newline at end of file
diff --git a/funasr/__init__.py b/funasr/__init__.py
index 669bdac..a5011bf 100644
--- a/funasr/__init__.py
+++ b/funasr/__init__.py
@@ -30,4 +30,5 @@
import_submodules(__name__)
-from funasr.bin.inference import AutoModel, AutoFrontend
\ No newline at end of file
+from funasr.auto.auto_model import AutoModel
+from funasr.auto.auto_frontend import AutoFrontend
\ No newline at end of file
diff --git a/funasr/auto/__init__.py b/funasr/auto/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/auto/__init__.py
diff --git a/funasr/auto/auto_frontend.py b/funasr/auto/auto_frontend.py
new file mode 100644
index 0000000..661f949
--- /dev/null
+++ b/funasr/auto/auto_frontend.py
@@ -0,0 +1,95 @@
+import json
+import time
+import torch
+import hydra
+import random
+import string
+import logging
+import os.path
+from tqdm import tqdm
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+from funasr.register import tables
+from funasr.utils.load_utils import load_bytes
+from funasr.download.file import download_from_url
+from funasr.download.download_from_hub import download_model
+from funasr.utils.vad_utils import slice_padding_audio_samples
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.timestamp_tools import timestamp_sentence
+from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+from funasr.models.campplus.cluster_backend import ClusterBackend
+from funasr.auto.auto_model import prepare_data_iterator
+
+
+
+class AutoFrontend:
+ def __init__(self, **kwargs):
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+ kwargs = download_model(**kwargs)
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+
+ self.frontend = frontend
+ if "frontend" in kwargs:
+ del kwargs["frontend"]
+ self.kwargs = kwargs
+
+
+ def __call__(self, input, input_len=None, kwargs=None, **cfg):
+
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len)
+ batch_size = kwargs.get("batch_size", 1)
+ device = kwargs.get("device", "cpu")
+ if device == "cpu":
+ batch_size = 1
+
+ meta_data = {}
+
+ result_list = []
+ num_samples = len(data_list)
+ pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
+
+ time0 = time.perf_counter()
+ for beg_idx in range(0, num_samples, batch_size):
+ end_idx = min(num_samples, beg_idx + batch_size)
+ data_batch = data_list[beg_idx:end_idx]
+ key_batch = key_list[beg_idx:end_idx]
+
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
+ frontend=self.frontend, **kwargs)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
+
+ speech.to(device=device), speech_lengths.to(device=device)
+ batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
+ result_list.append(batch)
+
+ pbar.update(1)
+ description = (
+ f"{meta_data}, "
+ )
+ pbar.set_description(description)
+
+ time_end = time.perf_counter()
+ pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
+
+ return result_list
+
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
new file mode 100644
index 0000000..580cca8
--- /dev/null
+++ b/funasr/auto/auto_model.py
@@ -0,0 +1,416 @@
+import json
+import time
+import torch
+import hydra
+import random
+import string
+import logging
+import os.path
+from tqdm import tqdm
+from omegaconf import DictConfig, OmegaConf, ListConfig
+
+from funasr.register import tables
+from funasr.utils.load_utils import load_bytes
+from funasr.download.file import download_from_url
+from funasr.download.download_from_hub import download_model
+from funasr.utils.vad_utils import slice_padding_audio_samples
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.timestamp_tools import timestamp_sentence
+from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+from funasr.models.campplus.cluster_backend import ClusterBackend
+
+
+def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
+ """
+
+ :param input:
+ :param input_len:
+ :param data_type:
+ :param frontend:
+ :return:
+ """
+ data_list = []
+ key_list = []
+ filelist = [".scp", ".txt", ".json", ".jsonl"]
+
+ chars = string.ascii_letters + string.digits
+ if isinstance(data_in, str) and data_in.startswith('http'): # url
+ data_in = download_from_url(data_in)
+ if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
+ _, file_extension = os.path.splitext(data_in)
+ file_extension = file_extension.lower()
+ if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
+ with open(data_in, encoding='utf-8') as fin:
+ for line in fin:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
+ lines = json.loads(line.strip())
+ data = lines["source"]
+ key = data["key"] if "key" in data else key
+ else: # filelist, wav.scp, text.txt: id \t data or data
+ lines = line.strip().split(maxsplit=1)
+ data = lines[1] if len(lines)>1 else lines[0]
+ key = lines[0] if len(lines)>1 else key
+
+ data_list.append(data)
+ key_list.append(key)
+ else:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+ elif isinstance(data_in, (list, tuple)):
+ if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
+ data_list_tmp = []
+ for data_in_i, data_type_i in zip(data_in, data_type):
+ key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
+ data_list_tmp.append(data_list_i)
+ data_list = []
+ for item in zip(*data_list_tmp):
+ data_list.append(item)
+ else:
+ # [audio sample point, fbank, text]
+ data_list = data_in
+ key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
+ else: # raw text; audio sample point, fbank; bytes
+ if isinstance(data_in, bytes): # audio bytes
+ data_in = load_bytes(data_in)
+ if key is None:
+ key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
+ data_list = [data_in]
+ key_list = [key]
+
+ return key_list, data_list
+
+
+class AutoModel:
+
+ def __init__(self, **kwargs):
+ tables.print()
+
+ model, kwargs = self.build_model(**kwargs)
+
+ # if vad_model is not None, build vad model else None
+ vad_model = kwargs.get("vad_model", None)
+ vad_kwargs = kwargs.get("vad_model_revision", None)
+ if vad_model is not None:
+ logging.info("Building VAD model.")
+ vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
+ vad_model, vad_kwargs = self.build_model(**vad_kwargs)
+
+ # if punc_model is not None, build punc model else None
+ punc_model = kwargs.get("punc_model", None)
+ punc_kwargs = kwargs.get("punc_model_revision", None)
+ if punc_model is not None:
+ logging.info("Building punc model.")
+ punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
+ punc_model, punc_kwargs = self.build_model(**punc_kwargs)
+
+ # if spk_model is not None, build spk model else None
+ spk_model = kwargs.get("spk_model", None)
+ spk_kwargs = kwargs.get("spk_model_revision", None)
+ if spk_model is not None:
+ logging.info("Building SPK model.")
+ spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
+ spk_model, spk_kwargs = self.build_model(**spk_kwargs)
+ self.cb_model = ClusterBackend()
+ spk_mode = kwargs.get("spk_mode", 'punc_segment')
+ if spk_mode not in ["default", "vad_segment", "punc_segment"]:
+ logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
+ self.spk_mode = spk_mode
+ self.preset_spk_num = kwargs.get("preset_spk_num", None)
+ if self.preset_spk_num:
+ logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
+ logging.warning("Many to print when using speaker model...")
+
+ self.kwargs = kwargs
+ self.model = model
+ self.vad_model = vad_model
+ self.vad_kwargs = vad_kwargs
+ self.punc_model = punc_model
+ self.punc_kwargs = punc_kwargs
+ self.spk_model = spk_model
+ self.spk_kwargs = spk_kwargs
+ self.model_path = kwargs["model_path"]
+
+
+ def build_model(self, **kwargs):
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+ kwargs = download_model(**kwargs)
+
+ set_all_random_seed(kwargs.get("seed", 0))
+
+ device = kwargs.get("device", "cuda")
+ if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
+ device = "cpu"
+ # kwargs["batch_size"] = 1
+ kwargs["device"] = device
+
+ if kwargs.get("ncpu", None):
+ torch.set_num_threads(kwargs.get("ncpu"))
+
+ # build tokenizer
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+ kwargs["token_list"] = tokenizer.token_list
+ vocab_size = len(tokenizer.token_list)
+ else:
+ vocab_size = -1
+
+ # build frontend
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"])
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+ model.eval()
+ model.to(device)
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ logging.info(f"Loading pretrained params from {init_param}")
+ load_pretrained_model(
+ model=model,
+ init_param=init_param,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ )
+
+ return model, kwargs
+
+ def __call__(self, *args, **cfg):
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ res = self.model(*args, kwargs)
+ return res
+
+
+
+ def generate(self, input, input_len=None, **cfg):
+ if self.vad_model is None:
+ return self.inference(input, input_len=input_len, **cfg)
+
+ else:
+ return self.inference_with_vad(input, input_len=input_len, **cfg)
+
+ def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
+ kwargs = self.kwargs if kwargs is None else kwargs
+ kwargs.update(cfg)
+ model = self.model if model is None else model
+
+ batch_size = kwargs.get("batch_size", 1)
+ # if kwargs.get("device", "cpu") == "cpu":
+ # batch_size = 1
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
+
+ speed_stats = {}
+ asr_result_list = []
+ num_samples = len(data_list)
+ pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
+ time_speech_total = 0.0
+ time_escape_total = 0.0
+ for beg_idx in range(0, num_samples, batch_size):
+ end_idx = min(num_samples, beg_idx + batch_size)
+ data_batch = data_list[beg_idx:end_idx]
+ key_batch = key_list[beg_idx:end_idx]
+ batch = {"data_in": data_batch, "key": key_batch}
+ if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
+ batch["data_in"] = data_batch[0]
+ batch["data_lengths"] = input_len
+
+ time1 = time.perf_counter()
+ with torch.no_grad():
+ results, meta_data = model.inference(**batch, **kwargs)
+ time2 = time.perf_counter()
+
+ asr_result_list.extend(results)
+ pbar.update(1)
+
+ # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
+ batch_data_time = meta_data.get("batch_data_time", -1)
+ time_escape = time2 - time1
+ speed_stats["load_data"] = meta_data.get("load_data", 0.0)
+ speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
+ speed_stats["forward"] = f"{time_escape:0.3f}"
+ speed_stats["batch_size"] = f"{len(results)}"
+ speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
+ description = (
+ f"{speed_stats}, "
+ )
+ pbar.set_description(description)
+ time_speech_total += batch_data_time
+ time_escape_total += time_escape
+
+ pbar.update(1)
+ pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
+ torch.cuda.empty_cache()
+ return asr_result_list
+
+ def inference_with_vad(self, input, input_len=None, **cfg):
+
+ # step.1: compute the vad model
+ self.vad_kwargs.update(cfg)
+ beg_vad = time.time()
+ res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
+ end_vad = time.time()
+ print(f"time cost vad: {end_vad - beg_vad:0.3f}")
+
+
+ # step.2 compute asr model
+ model = self.model
+ kwargs = self.kwargs
+ kwargs.update(cfg)
+ batch_size = int(kwargs.get("batch_size_s", 300))*1000
+ batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
+ kwargs["batch_size"] = batch_size
+
+ key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
+ results_ret_list = []
+ time_speech_total_all_samples = 0.0
+
+ beg_total = time.time()
+ pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
+ for i in range(len(res)):
+ key = res[i]["key"]
+ vadsegments = res[i]["value"]
+ input_i = data_list[i]
+ speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
+ speech_lengths = len(speech)
+ n = len(vadsegments)
+ data_with_index = [(vadsegments[i], i) for i in range(n)]
+ sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
+ results_sorted = []
+
+ if not len(sorted_data):
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
+ if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+ batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
+
+ batch_size_ms_cum = 0
+ beg_idx = 0
+ beg_asr_total = time.time()
+ time_speech_total_per_sample = speech_lengths/16000
+ time_speech_total_all_samples += time_speech_total_per_sample
+
+ for j, _ in enumerate(range(0, n)):
+ batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
+ if j < n - 1 and (
+ batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
+ sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
+ continue
+ batch_size_ms_cum = 0
+ end_idx = j + 1
+ speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
+ results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
+ if self.spk_model is not None:
+ all_segments = []
+ # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
+ for _b in range(len(speech_j)):
+ vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
+ sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
+ speech_j[_b]]]
+ segments = sv_chunk(vad_segments)
+ all_segments.extend(segments)
+ speech_b = [i[2] for i in segments]
+ spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
+ results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
+ beg_idx = end_idx
+ if len(results) < 1:
+ continue
+ results_sorted.extend(results)
+
+
+ pbar_total.update(1)
+ end_asr_total = time.time()
+ time_escape_total_per_sample = end_asr_total - beg_asr_total
+ pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
+ f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
+ f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+
+ restored_data = [0] * n
+ for j in range(n):
+ index = sorted_data[j][1]
+ restored_data[index] = results_sorted[j]
+ result = {}
+
+ # results combine for texts, timestamps, speaker embeddings and others
+ # TODO: rewrite for clean code
+ for j in range(n):
+ for k, v in restored_data[j].items():
+ if k.startswith("timestamp"):
+ if k not in result:
+ result[k] = []
+ for t in restored_data[j][k]:
+ t[0] += vadsegments[j][0]
+ t[1] += vadsegments[j][0]
+ result[k].extend(restored_data[j][k])
+ elif k == 'spk_embedding':
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
+ elif k == 'text':
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += " " + restored_data[j][k]
+ else:
+ if k not in result:
+ result[k] = restored_data[j][k]
+ else:
+ result[k] += restored_data[j][k]
+
+ # step.3 compute punc model
+ if self.punc_model is not None:
+ self.punc_kwargs.update(cfg)
+ punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+ result["text_with_punc"] = punc_res[0]["text"]
+
+ # speaker embedding cluster after resorted
+ if self.spk_model is not None:
+ all_segments = sorted(all_segments, key=lambda x: x[0])
+ spk_embedding = result['spk_embedding']
+ labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
+ del result['spk_embedding']
+ sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
+ if self.spk_mode == 'vad_segment':
+ sentence_list = []
+ for res, vadsegment in zip(restored_data, vadsegments):
+ sentence_list.append({"start": vadsegment[0],\
+ "end": vadsegment[1],
+ "sentence": res['text'],
+ "timestamp": res['timestamp']})
+ else: # punc_segment
+ sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+ result['timestamp'], \
+ result['text'])
+ distribute_spk(sentence_list, sv_output)
+ result['sentence_info'] = sentence_list
+
+ result["key"] = key
+ results_ret_list.append(result)
+ pbar_total.update(1)
+
+ pbar_total.update(1)
+ end_total = time.time()
+ time_escape_total_all_samples = end_total - beg_total
+ pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
+ f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
+ f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
+ return results_ret_list
+
diff --git a/funasr/auto/auto_tokenizer.py b/funasr/auto/auto_tokenizer.py
new file mode 100644
index 0000000..d5082e2
--- /dev/null
+++ b/funasr/auto/auto_tokenizer.py
@@ -0,0 +1,8 @@
+
+
+class AutoTokenizer:
+ """
+ Undo
+ """
+ def __init__(self):
+ pass
\ No newline at end of file
diff --git a/funasr/bin/inference.py b/funasr/bin/inference.py
index 7368d16..d2f0c14 100644
--- a/funasr/bin/inference.py
+++ b/funasr/bin/inference.py
@@ -1,87 +1,9 @@
-import json
-import time
-import torch
import hydra
-import random
-import string
import logging
-import os.path
-from tqdm import tqdm
from omegaconf import DictConfig, OmegaConf, ListConfig
-from funasr.register import tables
-from funasr.utils.load_utils import load_bytes
-from funasr.download.file import download_from_url
-from funasr.download.download_from_hub import download_model
-from funasr.utils.vad_utils import slice_padding_audio_samples
-from funasr.train_utils.set_all_random_seed import set_all_random_seed
-from funasr.train_utils.load_pretrained_model import load_pretrained_model
-from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
-from funasr.utils.timestamp_tools import timestamp_sentence
-from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
-from funasr.models.campplus.cluster_backend import ClusterBackend
+from funasr.auto.auto_model import AutoModel
-
-def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
- """
-
- :param input:
- :param input_len:
- :param data_type:
- :param frontend:
- :return:
- """
- data_list = []
- key_list = []
- filelist = [".scp", ".txt", ".json", ".jsonl"]
-
- chars = string.ascii_letters + string.digits
- if isinstance(data_in, str) and data_in.startswith('http'): # url
- data_in = download_from_url(data_in)
- if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
- _, file_extension = os.path.splitext(data_in)
- file_extension = file_extension.lower()
- if file_extension in filelist: #filelist: wav.scp, file.jsonl;text.txt;
- with open(data_in, encoding='utf-8') as fin:
- for line in fin:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- if data_in.endswith(".jsonl"): #file.jsonl: json.dumps({"source": data})
- lines = json.loads(line.strip())
- data = lines["source"]
- key = data["key"] if "key" in data else key
- else: # filelist, wav.scp, text.txt: id \t data or data
- lines = line.strip().split(maxsplit=1)
- data = lines[1] if len(lines)>1 else lines[0]
- key = lines[0] if len(lines)>1 else key
-
- data_list.append(data)
- key_list.append(key)
- else:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- data_list = [data_in]
- key_list = [key]
- elif isinstance(data_in, (list, tuple)):
- if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
- data_list_tmp = []
- for data_in_i, data_type_i in zip(data_in, data_type):
- key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
- data_list_tmp.append(data_list_i)
- data_list = []
- for item in zip(*data_list_tmp):
- data_list.append(item)
- else:
- # [audio sample point, fbank, text]
- data_list = data_in
- key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
- else: # raw text; audio sample point, fbank; bytes
- if isinstance(data_in, bytes): # audio bytes
- data_in = load_bytes(data_in)
- if key is None:
- key = "rand_key_" + ''.join(random.choice(chars) for _ in range(13))
- data_list = [data_in]
- key_list = [key]
-
- return key_list, data_list
@hydra.main(config_name=None, version_base=None)
def main_hydra(cfg: DictConfig):
@@ -101,400 +23,8 @@
if kwargs.get("debug", False):
import pdb; pdb.set_trace()
model = AutoModel(**kwargs)
- res = model(input=kwargs["input"])
+ res = model.generate(input=kwargs["input"])
print(res)
-
-class AutoModel:
-
- def __init__(self, **kwargs):
- tables.print()
-
- model, kwargs = self.build_model(**kwargs)
-
- # if vad_model is not None, build vad model else None
- vad_model = kwargs.get("vad_model", None)
- vad_kwargs = kwargs.get("vad_model_revision", None)
- if vad_model is not None:
- logging.info("Building VAD model.")
- vad_kwargs = {"model": vad_model, "model_revision": vad_kwargs}
- vad_model, vad_kwargs = self.build_model(**vad_kwargs)
-
- # if punc_model is not None, build punc model else None
- punc_model = kwargs.get("punc_model", None)
- punc_kwargs = kwargs.get("punc_model_revision", None)
- if punc_model is not None:
- logging.info("Building punc model.")
- punc_kwargs = {"model": punc_model, "model_revision": punc_kwargs}
- punc_model, punc_kwargs = self.build_model(**punc_kwargs)
-
- # if spk_model is not None, build spk model else None
- spk_model = kwargs.get("spk_model", None)
- spk_kwargs = kwargs.get("spk_model_revision", None)
- if spk_model is not None:
- logging.info("Building SPK model.")
- spk_kwargs = {"model": spk_model, "model_revision": spk_kwargs}
- spk_model, spk_kwargs = self.build_model(**spk_kwargs)
- self.cb_model = ClusterBackend()
- spk_mode = kwargs.get("spk_mode", 'punc_segment')
- if spk_mode not in ["default", "vad_segment", "punc_segment"]:
- logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
- self.spk_mode = spk_mode
- self.preset_spk_num = kwargs.get("preset_spk_num", None)
- if self.preset_spk_num:
- logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
- logging.warning("Many to print when using speaker model...")
-
- self.kwargs = kwargs
- self.model = model
- self.vad_model = vad_model
- self.vad_kwargs = vad_kwargs
- self.punc_model = punc_model
- self.punc_kwargs = punc_kwargs
- self.spk_model = spk_model
- self.spk_kwargs = spk_kwargs
- self.model_path = kwargs["model_path"]
-
-
- def build_model(self, **kwargs):
- assert "model" in kwargs
- if "model_conf" not in kwargs:
- logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
- kwargs = download_model(**kwargs)
-
- set_all_random_seed(kwargs.get("seed", 0))
-
- device = kwargs.get("device", "cuda")
- if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
- device = "cpu"
- # kwargs["batch_size"] = 1
- kwargs["device"] = device
-
- if kwargs.get("ncpu", None):
- torch.set_num_threads(kwargs.get("ncpu"))
-
- # build tokenizer
- tokenizer = kwargs.get("tokenizer", None)
- if tokenizer is not None:
- tokenizer_class = tables.tokenizer_classes.get(tokenizer)
- tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
- kwargs["tokenizer"] = tokenizer
- kwargs["token_list"] = tokenizer.token_list
- vocab_size = len(tokenizer.token_list)
- else:
- vocab_size = -1
-
- # build frontend
- frontend = kwargs.get("frontend", None)
- if frontend is not None:
- frontend_class = tables.frontend_classes.get(frontend)
- frontend = frontend_class(**kwargs["frontend_conf"])
- kwargs["frontend"] = frontend
- kwargs["input_size"] = frontend.output_size()
-
- # build model
- model_class = tables.model_classes.get(kwargs["model"])
- model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
- model.eval()
- model.to(device)
-
- # init_param
- init_param = kwargs.get("init_param", None)
- if init_param is not None:
- logging.info(f"Loading pretrained params from {init_param}")
- load_pretrained_model(
- model=model,
- init_param=init_param,
- ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
- oss_bucket=kwargs.get("oss_bucket", None),
- )
-
- return model, kwargs
-
- def __call__(self, input, input_len=None, **cfg):
- if self.vad_model is None:
- return self.generate(input, input_len=input_len, **cfg)
-
- else:
- return self.generate_with_vad(input, input_len=input_len, **cfg)
-
- def generate(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
- kwargs = self.kwargs if kwargs is None else kwargs
- kwargs.update(cfg)
- model = self.model if model is None else model
-
- batch_size = kwargs.get("batch_size", 1)
- # if kwargs.get("device", "cpu") == "cpu":
- # batch_size = 1
-
- key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
-
- speed_stats = {}
- asr_result_list = []
- num_samples = len(data_list)
- pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
- time_speech_total = 0.0
- time_escape_total = 0.0
- for beg_idx in range(0, num_samples, batch_size):
- end_idx = min(num_samples, beg_idx + batch_size)
- data_batch = data_list[beg_idx:end_idx]
- key_batch = key_list[beg_idx:end_idx]
- batch = {"data_in": data_batch, "key": key_batch}
- if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank
- batch["data_in"] = data_batch[0]
- batch["data_lengths"] = input_len
-
- time1 = time.perf_counter()
- with torch.no_grad():
- results, meta_data = model.inference(**batch, **kwargs)
- time2 = time.perf_counter()
-
- asr_result_list.extend(results)
- pbar.update(1)
-
- # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
- batch_data_time = meta_data.get("batch_data_time", -1)
- time_escape = time2 - time1
- speed_stats["load_data"] = meta_data.get("load_data", 0.0)
- speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
- speed_stats["forward"] = f"{time_escape:0.3f}"
- speed_stats["batch_size"] = f"{len(results)}"
- speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
- description = (
- f"{speed_stats}, "
- )
- pbar.set_description(description)
- time_speech_total += batch_data_time
- time_escape_total += time_escape
-
- pbar.update(1)
- pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
- torch.cuda.empty_cache()
- return asr_result_list
-
- def generate_with_vad(self, input, input_len=None, **cfg):
-
- # step.1: compute the vad model
- self.vad_kwargs.update(cfg)
- beg_vad = time.time()
- res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
- end_vad = time.time()
- print(f"time cost vad: {end_vad - beg_vad:0.3f}")
-
-
- # step.2 compute asr model
- model = self.model
- kwargs = self.kwargs
- kwargs.update(cfg)
- batch_size = int(kwargs.get("batch_size_s", 300))*1000
- batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
- kwargs["batch_size"] = batch_size
-
- key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
- results_ret_list = []
- time_speech_total_all_samples = 0.0
-
- beg_total = time.time()
- pbar_total = tqdm(colour="red", total=len(res) + 1, dynamic_ncols=True)
- for i in range(len(res)):
- key = res[i]["key"]
- vadsegments = res[i]["value"]
- input_i = data_list[i]
- speech = load_audio_text_image_video(input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000))
- speech_lengths = len(speech)
- n = len(vadsegments)
- data_with_index = [(vadsegments[i], i) for i in range(n)]
- sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
- results_sorted = []
-
- if not len(sorted_data):
- logging.info("decoding, utt: {}, empty speech".format(key))
- continue
-
- if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
- batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
-
- batch_size_ms_cum = 0
- beg_idx = 0
- beg_asr_total = time.time()
- time_speech_total_per_sample = speech_lengths/16000
- time_speech_total_all_samples += time_speech_total_per_sample
-
- for j, _ in enumerate(range(0, n)):
- batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
- if j < n - 1 and (
- batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
- sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_threshold_ms:
- continue
- batch_size_ms_cum = 0
- end_idx = j + 1
- speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
- results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
- if self.spk_model is not None:
- all_segments = []
- # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
- for _b in range(len(speech_j)):
- vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
- sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
- speech_j[_b]]]
- segments = sv_chunk(vad_segments)
- all_segments.extend(segments)
- speech_b = [i[2] for i in segments]
- spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
- results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
- beg_idx = end_idx
- if len(results) < 1:
- continue
- results_sorted.extend(results)
-
-
- pbar_total.update(1)
- end_asr_total = time.time()
- time_escape_total_per_sample = end_asr_total - beg_asr_total
- pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
- f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
- f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
-
- restored_data = [0] * n
- for j in range(n):
- index = sorted_data[j][1]
- restored_data[index] = results_sorted[j]
- result = {}
-
- # results combine for texts, timestamps, speaker embeddings and others
- # TODO: rewrite for clean code
- for j in range(n):
- for k, v in restored_data[j].items():
- if k.startswith("timestamp"):
- if k not in result:
- result[k] = []
- for t in restored_data[j][k]:
- t[0] += vadsegments[j][0]
- t[1] += vadsegments[j][0]
- result[k].extend(restored_data[j][k])
- elif k == 'spk_embedding':
- if k not in result:
- result[k] = restored_data[j][k]
- else:
- result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
- elif k == 'text':
- if k not in result:
- result[k] = restored_data[j][k]
- else:
- result[k] += " " + restored_data[j][k]
- else:
- if k not in result:
- result[k] = restored_data[j][k]
- else:
- result[k] += restored_data[j][k]
-
- # step.3 compute punc model
- if self.punc_model is not None:
- self.punc_kwargs.update(cfg)
- punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
- result["text_with_punc"] = punc_res[0]["text"]
-
- # speaker embedding cluster after resorted
- if self.spk_model is not None:
- all_segments = sorted(all_segments, key=lambda x: x[0])
- spk_embedding = result['spk_embedding']
- labels = self.cb_model(spk_embedding, oracle_num=self.preset_spk_num)
- del result['spk_embedding']
- sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
- if self.spk_mode == 'vad_segment':
- sentence_list = []
- for res, vadsegment in zip(restored_data, vadsegments):
- sentence_list.append({"start": vadsegment[0],\
- "end": vadsegment[1],
- "sentence": res['text'],
- "timestamp": res['timestamp']})
- else: # punc_segment
- sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
- result['timestamp'], \
- result['text'])
- distribute_spk(sentence_list, sv_output)
- result['sentence_info'] = sentence_list
-
- result["key"] = key
- results_ret_list.append(result)
- pbar_total.update(1)
-
- pbar_total.update(1)
- end_total = time.time()
- time_escape_total_all_samples = end_total - beg_total
- pbar_total.set_description(f"rtf_avg_all_samples: {time_escape_total_all_samples / time_speech_total_all_samples:0.3f}, "
- f"time_speech_total_all_samples: {time_speech_total_all_samples: 0.3f}, "
- f"time_escape_total_all_samples: {time_escape_total_all_samples:0.3f}")
- return results_ret_list
-
-
-class AutoFrontend:
- def __init__(self, **kwargs):
- assert "model" in kwargs
- if "model_conf" not in kwargs:
- logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
- kwargs = download_model(**kwargs)
-
- # build frontend
- frontend = kwargs.get("frontend", None)
- if frontend is not None:
- frontend_class = tables.frontend_classes.get(frontend)
- frontend = frontend_class(**kwargs["frontend_conf"])
-
- self.frontend = frontend
- if "frontend" in kwargs:
- del kwargs["frontend"]
- self.kwargs = kwargs
-
-
- def __call__(self, input, input_len=None, kwargs=None, **cfg):
-
- kwargs = self.kwargs if kwargs is None else kwargs
- kwargs.update(cfg)
-
-
- key_list, data_list = prepare_data_iterator(input, input_len=input_len)
- batch_size = kwargs.get("batch_size", 1)
- device = kwargs.get("device", "cpu")
- if device == "cpu":
- batch_size = 1
-
- meta_data = {}
-
- result_list = []
- num_samples = len(data_list)
- pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
-
- time0 = time.perf_counter()
- for beg_idx in range(0, num_samples, batch_size):
- end_idx = min(num_samples, beg_idx + batch_size)
- data_batch = data_list[beg_idx:end_idx]
- key_batch = key_list[beg_idx:end_idx]
-
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_batch, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=self.frontend, **kwargs)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
-
- speech.to(device=device), speech_lengths.to(device=device)
- batch = {"input": speech, "input_len": speech_lengths, "key": key_batch}
- result_list.append(batch)
-
- pbar.update(1)
- description = (
- f"{meta_data}, "
- )
- pbar.set_description(description)
-
- time_end = time.perf_counter()
- pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
-
- return result_list
if __name__ == '__main__':
--
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