update

.gitignore

@@ -15,6 +15,7 @@
 /trained_models/
 /temp/
 
+**/*.csv
+**/*.onnx
 #**/*.wav
 **/*.xlsx
-**/*.onnx

examples/download_wav/Temp Query 5_20251008-093912.csv

@@ -0,0 +1,101 @@
+date,overdue_term,id,case_id,credit_user_id,call_start_timestamp,call_end_timestamp,thirdpart_download_url
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+11/10/2025,M3,201549933,62873165,1.87383E+18,1760149739,1760149747,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/fd0caad7-72fd-4152-a8ed-58253e946567.mp3
+10/10/2025,M5,201447596,58417708,1.9626E+18,1760085087,1760085092,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251010/21962/0f8f64c4-2587-4501-abb8-23e0e5389635.mp3
+11/10/2025,M1,201596157,65991243,1.88004E+18,1760161446,1760161452,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/8f731992-cbc6-45e1-8203-5628d51a6e45.mp3
+10/10/2025,M2,201391828,63462209,1.1835E+16,1760067513,1760067535,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251010/21962/2c641568-ebf6-4989-abeb-e2bc404d79e8.mp3
+11/10/2025,M4,201579066,60241526,1.95443E+18,1760157061,1760157070,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/34c79266-7c59-4e26-b936-05d64716fa79.mp3
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+11/10/2025,M5,201607636,57899370,1.93241E+18,1760163267,1760163287,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/d8e13804-692a-4cf7-b4e2-781384a1d559.mp3
+10/10/2025,M4,201426209,60181850,1.79511E+18,1760080075,1760080081,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251010/21962/8f81c559-7392-46ff-8ebb-1a6edc41381c.mp3
+11/10/2025,M1,201535197,66655594,1.66159E+18,1760147133,1760147139,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/d793a0e3-bd7b-44f3-a1e2-bc1eb0908c1a.mp3
+11/10/2025,M4,201613127,61191667,1.78852E+18,1760164234,1760164240,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/4e794fb2-01bf-41ff-843f-945b2b1ec9df.mp3
+10/10/2025,M3,201456582,61353852,1.70556E+18,1760087088,1760087093,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251010/21962/5fe67f28-5f86-43bb-a87f-589b75118d56.mp3
+11/10/2025,M2,201536974,63373730,2.02636E+18,1760147420,1760147433,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/cf2bf8cc-e2af-478a-96e4-686c59a98d4d.mp3
+11/10/2025,M6,201598303,57270639,1.57805E+18,1760161833,1760161850,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/07e2de01-8040-4942-8586-77d6eb38b64f.mp3
+11/10/2025,M4,201577614,60545450,1.95248E+18,1760156607,1760156635,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/53f6d6eb-c22e-4935-9d3c-941969a0241f.mp3
+10/10/2025,M1,201451460,66451819,1406890,1760085972,1760085978,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251010/21962/43061915-f8e0-405b-9be0-bd3826d0aa69.mp3
+11/10/2025,M5,201566778,57480954,1.79569E+18,1760154035,1760154041,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/f737520a-d62f-4c71-a81c-bfbcbd2887d3.mp3
+11/10/2025,M5,201579474,58000396,1.17763E+16,1760157205,1760157215,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/b8d5a9b1-13cf-4667-8271-1b562de5dd22.mp3
+11/10/2025,M1,201547564,66391023,987018,1760149263,1760149269,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/434cea47-98a9-4f24-b29e-bee36e9f9832.mp3
+11/10/2025,M5,201538978,57340689,1.62497E+18,1760147736,1760147755,https://idn1.obs.ap-southeast-4.myhuaweicloud.com/20251011/21962/a74e253e-34cf-42df-9c3c-c8f6a45b994e.mp3

examples/download_wav/step_1_download_wav.py

@@ -31,7 +31,7 @@ def get_args():
     )
     parser.add_argument(
         "--output_dir",
-        default=(project_path / "data/calling/358/wav_2ch").as_posix(),
+        default=(project_path / "data/calling/62/wav_2ch").as_posix(),
         type=str
     )
     args = parser.parse_args()
@@ -39,37 +39,7 @@ def get_args():
 
 
 excel_file_str = """
-AIAgent-CallLog-20250929100824.xlsx
-AIAgent-CallLog-20250929134959.xlsx
-AIAgent-CallLog-20250929135030.xlsx
-AIAgent-CallLog-20250929135052.xlsx
-AIAgent-CallLog-20250929135122.xlsx
-AIAgent-CallLog-20250929135134.xlsx
-AIAgent-CallLog-20250929135209.xlsx
-AIAgent-CallLog-20250929135219.xlsx
-AIAgent-CallLog-20250929135247.xlsx
-AIAgent-CallLog-20250929135300.xlsx
-AIAgent-CallLog-20250929135311.xlsx
-AIAgent-CallLog-20250929135335.xlsx
-AIAgent-CallLog-20250929135344.xlsx
-AIAgent-CallLog-20250929135355.xlsx
-AIAgent-CallLog-20250929135443.xlsx
-AIAgent-CallLog-20250929135452.xlsx
-AIAgent-CallLog-20250929135501.xlsx
-AIAgent-CallLog-20250929135537.xlsx
-AIAgent-CallLog-20250929135544.xlsx
-AIAgent-CallLog-20250929135554.xlsx
-AIAgent-CallLog-20250929135630.xlsx
-AIAgent-CallLog-20250929135701.xlsx
-AIAgent-CallLog-20250929135710.xlsx
-AIAgent-CallLog-20250929135716.xlsx
-AIAgent-CallLog-20250929135755.xlsx
-AIAgent-CallLog-20250929135800.xlsx
-AIAgent-CallLog-20250929135809.xlsx
-AIAgent-CallLog-20250929135842.xlsx
-AIAgent-CallLog-20250929135849.xlsx
-AIAgent-CallLog-20250929135858.xlsx
-AIAgent-CallLog-20250929135909.xlsx
+Temp Query 5_20251008-093912.csv
 """
 
 
@@ -101,11 +71,16 @@ def main():
             continue
         excel_file = excel_file_dir / name
 
-        df = pd.read_excel(excel_file.as_posix())
+        # df = pd.read_excel(excel_file.as_posix())
+        df = pd.read_csv(excel_file.as_posix())
         for i, row in tqdm(df.iterrows()):
-            call_date = row["Attempt time"]
-            call_id = row["Call ID"]
-            record_url = row["Recording file"]
+            call_date = "2025-10-12 00:00:00"
+            record_url = row["thirdpart_download_url"]
+            call_id = Path(record_url).stem
+
+            # call_date = row["Attempt time"]
+            # call_id = row["Call ID"]
+            # record_url = row["Recording file"]
             if pd.isna(record_url):
                 continue
 
@@ -137,7 +112,7 @@ def main():
             if resp.status_code != 200:
                 raise AssertionError("status_code: {}; text: {}".format(resp.status_code, resp.text))
 
-            filename = output_dir / "{}.wav".format(call_id)
+            filename = output_dir / "{}.mp3".format(call_id)
             with open(filename.as_posix(), "wb") as f:
                 f.write(resp.content)
 

examples/download_wav/step_2_to_1ch.py

@@ -3,8 +3,9 @@
 import argparse
 import os
 from pathlib import Path
-import time
 
+import librosa
+import numpy as np
 from scipy.io import wavfile
 from tqdm import tqdm
 
@@ -16,12 +17,12 @@ def get_args():
 
     parser.add_argument(
         "--audio_dir",
-        default=(project_path / "data/calling/358/wav_2ch").as_posix(),
+        default=(project_path / "data/calling/62/wav_2ch").as_posix(),
         type=str
     )
     parser.add_argument(
         "--output_dir",
-        default=(project_path / "data/calling/358/wav_1ch").as_posix(),
+        default=(project_path / "data/calling/62/wav_1ch").as_posix(),
         type=str
     )
     args = parser.parse_args()
@@ -36,13 +37,13 @@ def main():
     output_dir.mkdir(parents=True, exist_ok=True)
 
     finished = set()
-    for filename in tqdm(list(output_dir.glob("*.wav"))):
+    for filename in tqdm(list(output_dir.glob("*.mp3"))):
         splits = filename.stem.split("_")
         call_id = splits[3]
         finished.add(call_id)
     print(f"finished count: {len(finished)}")
 
-    for filename in tqdm(list(audio_dir.glob("*.wav"))):
+    for filename in tqdm(list(audio_dir.glob("*.mp3"))):
         call_id = filename.stem
 
         if call_id in finished:
@@ -51,16 +52,19 @@ def main():
         finished.add(call_id)
 
         try:
-            sample_rate, signal = wavfile.read(filename.as_posix())
+            # sample_rate, signal = wavfile.read(filename.as_posix())
+            signal, sample_rate = librosa.load(filename.as_posix(), sr=8000, mono=False)
+            signal = np.array(signal * (1 << 15), dtype=np.int16)
         except UnboundLocalError as error:
             print(f"wavfile read failed. error type: {type(error)}, text: {str(error)}, filename: {filename.as_posix()}")
             raise error
         if sample_rate != 8000:
             raise AssertionError
 
-        signal = signal[:, 0]
+        # signal = signal[:, 0]
+        signal = signal[0, :]
 
-        to_filename = output_dir / f"active_media_r_{call_id}_fi-FI_none.wav"
+        to_filename = output_dir / f"active_media_r_{call_id}_id-ID_none.wav"
         try:
             wavfile.write(
                 to_filename.as_posix(),

examples/download_wav/step_3_split_two_second_wav.py

@@ -15,17 +15,21 @@ def get_args():
 
     parser.add_argument(
         "--audio_dir",
-        default=(project_path / "data/calling/358/wav_2ch").as_posix(),
+        # default=(project_path / "data/calling/66/wav_1ch").as_posix(),
+        # default=(project_path / "data/calling/358/wav_1ch/finished/voicemail_annotation").as_posix(),
+        # default=(project_path / "data/calling/358/wav_1ch/finished/voicemail_annotation").as_posix(),
+        default=r"D:\Users\tianx\HuggingSpaces\template_match_asr\data\wav\early_media\52\music",
         type=str
     )
     parser.add_argument(
         "--output_dir",
-        default=(project_path / "data/calling/358/wav_segmented").as_posix(),
+        # default=(project_path / "data/calling/358/wav_segmented").as_posix(),
+        default=r"D:\Users\tianx\HuggingSpaces\template_match_asr\data\wav\early_media\52\music\wav_segmented",
         type=str
     )
     parser.add_argument(
         "--first_n_seconds",
-        default=8,
+        default=1000,
         type=int
     )
     args = parser.parse_args()
@@ -40,12 +44,16 @@ def main():
     output_dir.mkdir(parents=True, exist_ok=True)
 
     for filename in tqdm(list(audio_dir.glob("*.wav"))):
-        call_id = filename.stem
+        splits = filename.stem.split("_")
+        call_id = splits[3]
+        language = splits[4]
+        scene_id = splits[5]
+
         sample_rate, signal = wavfile.read(filename.as_posix())
         if sample_rate != 8000:
             raise AssertionError
 
-        signal = signal[:, 0]
+        # signal = signal[:, 0]
         signal_length = len(signal) - sample_rate * 2
         if signal_length <= 0:
             continue
@@ -56,8 +64,7 @@ def main():
             end = begin + sample_rate * 2
             sub_signal = signal[begin: end]
 
-            ts = int(time.time() * 1000)
-            to_filename = output_dir / "{}_fi-FI_none_{}.wav".format(call_id, ts)
+            to_filename = output_dir / f"active_media_r_{call_id}_{language}_{scene_id}_{begin}.wav"
             wavfile.write(
                 to_filename.as_posix(),
                 sample_rate,

examples/lstm_badcase_filter/step_1_badcase_filter.py

@@ -0,0 +1,233 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from pathlib import Path
+
+from tqdm import tqdm
+
+from gradio_client import Client, handle_file
+import librosa
+import numpy as np
+import onnxruntime as ort
+from scipy.io import wavfile
+import torch
+import torchaudio
+import shutil
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--audio_dir",
+        # default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\pt-BR2",
+        # default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\pt-BR",
+        default=r"D:\Users\tianx\HuggingDatasets\calling_analysis\data\pt-BR\bell_and_di_then_mute",
+        type=str,
+    )
+    parser.add_argument(
+        "--onnx_model_file",
+        # default=(project_path / "examples/online_model_test/models/pt-BR.onnx").as_posix(),
+        default="../online_model_test/models/pt-BR.onnx",
+        type=str
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=(project_path / "data/badcase").as_posix(),
+        type=str,
+    )
+    args = parser.parse_args()
+    return args
+
+
+class OnlineModelConfig(object):
+    def __init__(self,
+                 sample_rate: int = 8000,
+                 n_fft: int = 1024,
+                 hop_size: int = 512,
+                 n_mels: int = 80,
+                 f_min: float = 10.0,
+                 f_max: float = 3800.0,
+                 ):
+        self.sample_rate = sample_rate
+        self.n_fft = n_fft
+        self.hop_size = hop_size
+        self.n_mels = n_mels
+        self.f_min = f_min
+        self.f_max = f_max
+
+
+class OnlineModelInference(object):
+    def __init__(self,
+                 model_path: str,
+                 ):
+        self.model_path = model_path
+
+        providers = [
+            "CUDAExecutionProvider", "CPUExecutionProvider"
+        ] if torch.cuda.is_available() else [
+            "CPUExecutionProvider"
+        ]
+        self.session = ort.InferenceSession(self.model_path, providers=providers)
+
+        self.config = OnlineModelConfig()
+
+        self.mel_transform = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.config.sample_rate,
+            n_fft=self.config.n_fft,
+            hop_length=self.config.hop_size,
+            n_mels=self.config.n_mels,
+            f_min=self.config.f_min,
+            f_max=self.config.f_max,
+            window_fn=torch.hamming_window
+        )
+
+    def predict_by_ndarray(self,
+                           sub_signal: np.ndarray,
+                           h: np.ndarray = None,
+                           c: np.ndarray = None,
+                           ):
+        # sub_signal, shape: [num_samples,]
+        sub_signal = torch.tensor(sub_signal, dtype=torch.float32)
+
+        sub_signal = sub_signal.unsqueeze(0)
+        # sub_signal, shape: [1, num_samples]
+        mel_spec = self.mel_transform.forward(sub_signal)
+        # mel_spec, shape: [1, n_mels, n_frames]
+        mel_spec = torch.transpose(mel_spec, dim0=1, dim1=2)
+        # mel_spec, shape: [1, n_frames, n_mels]
+
+        h = torch.tensor(h) if h is not None else None
+        c = torch.tensor(c) if h is not None else None
+        label, prob, h, c = self.predict_by_mel_spec(mel_spec, h=h, c=c)
+        # h, c: torch.Tensor
+        h = h.numpy()
+        c = c.numpy()
+        return label, prob, h, c
+
+    def predict_by_mel_spec(self,
+                            mel_spec: torch.Tensor,
+                            h: torch.Tensor = None,
+                            c: torch.Tensor = None,
+                            ):
+        # mel_spec, shape: [1, n_frames, n_mels]
+
+        if h is None:
+            h = np.zeros((3, 1, 64), dtype=np.float32)  # 3层LSTM，批次大小1，隐藏大小64
+        else:
+            h = h.numpy()
+        if c is None:
+            c = np.zeros((3, 1, 64), dtype=np.float32)  # 3层LSTM，批次大小1，隐藏大小64
+        else:
+            c = c.numpy()
+
+        mel_spec_np = mel_spec.numpy()
+        outputs = self.session.run(
+            input_feed={
+                "input": mel_spec_np,
+                "h": h,
+                "c": c
+            },
+            output_names=[
+                "output", "h_out", "c_out"
+            ],
+        )
+        logits, h, c = outputs
+        # logits, np.ndarray, shape: [b, num_labels]
+        # h, c: np.ndarray
+        h = torch.tensor(h)
+        c = torch.tensor(c)
+
+        probs = torch.softmax(torch.tensor(logits), dim=1)
+        max_prob, predicted_label_index = torch.max(probs, dim=1)
+
+        label = self.get_label_by_index(predicted_label_index.item())
+        prob = max_prob.item()
+        return label, prob, h, c
+
+    @staticmethod
+    def get_label_by_index(index: int):
+        label_map = {
+            0: "voice",
+            1: "voicemail",
+            2: "mute",
+            3: "noise"
+        }
+        result = label_map[index]
+        return result
+
+
+def main():
+    args = get_args()
+
+    client = Client("http://127.0.0.1:7864/")
+    # client = Client("http://10.75.27.247:7864/")
+
+    audio_dir = Path(args.audio_dir)
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    model = OnlineModelInference(model_path=args.onnx_model_file)
+
+    for filename in tqdm(audio_dir.glob("**/active_media_r_*.wav")):
+        splits = filename.stem.split("_")
+        call_id = splits[3]
+        language = splits[4]
+        scene_id = splits[5]
+
+        signal, sample_rate = librosa.load(filename.as_posix(), sr=8000)
+        duration = librosa.get_duration(y=signal, sr=sample_rate)
+        signal_length = len(signal)
+        if signal_length == 0:
+            continue
+
+        begin = 0
+        end = begin + sample_rate * 2
+        sub_signal = signal[begin: end]
+        if sub_signal.shape[0] != 16000:
+            continue
+
+        h = None
+        c = None
+        label1, prob1, h, c = model.predict_by_ndarray(sub_signal, h=h, c=c)
+
+        sub_signal_ = np.array(sub_signal * (1 << 15), dtype=np.int16)
+        temp_file = "temp.wav"
+
+        wavfile.write(
+            temp_file,
+            8000,
+            sub_signal_,
+        )
+
+        # label2, prob2 = client.predict(
+        #     audio_t=handle_file(temp_file),
+        #     model_name="voicemail-pt-br-2-ch4",
+        #     ground_true="Hello!!",
+        #     api_name="/when_click_cls_button"
+        # )
+        label2, prob2 = client.predict(
+            audio_t=handle_file(temp_file),
+            model_name="sound-8-ch4",
+            ground_true="Hello!!",
+            api_name="/when_click_cls_button"
+        )
+
+        print(label1)
+        print(label2)
+        label2 = "voicemail"
+        label1 = "non_voicemail"
+        if label2 in ("voicemail", "bell") and label1 != "voicemail":
+            tgt_file = output_dir / f"active_media_r_{call_id}_{language}_{scene_id}_0.wav"
+            if not tgt_file.exists():
+                shutil.move(
+                    temp_file,
+                    tgt_file.as_posix(),
+                )
+
+    return
+
+
+if __name__ == "__main__":
+    main()

examples/online_model_test/step_1_predict.py

@@ -19,13 +19,13 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--audio_dir",
-        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\zh-TW",
+        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\th-TH\th-TH\early_media_no_voice",
         type=str,
     )
-    parser.add_argument("--onnx_model_file", default="zh-TW.onnx", type=str)
+    parser.add_argument("--onnx_model_file", default="models/th-TH.onnx", type=str)
     parser.add_argument("--target_duration", default=8.0, type=float)
 
-    parser.add_argument("--output_file", default="zh_tw_predict.xlsx", type=str)
+    parser.add_argument("--output_file", default="th-TH_predict.xlsx", type=str)
 
     args = parser.parse_args()
     return args
@@ -177,13 +177,15 @@ def main():
         for begin in range(0, target_duration, sample_rate*2):
             end = begin + sample_rate*2
             sub_signal = signal[begin: end]
-            if len(sub_signal) == 0:
+            if len(sub_signal) < 0.5 * sample_rate:
                 break
             label, prob, h, c = model.predict_by_ndarray(sub_signal, h=h, c=c)
             predict_result.append({
                 "label": label,
                 "prob": prob,
             })
+        if len(predict_result) == 0:
+            continue
         label_list = [p["label"] for p in predict_result]
         predict_result_ = json.dumps(predict_result, ensure_ascii=False, indent=4)
         label2 = predict_result[0]["label"]

examples/online_model_test/step_2_audio_filter.py

@@ -10,10 +10,10 @@ import pandas as pd
 def get_args():
     parser = argparse.ArgumentParser()
 
-    parser.add_argument("--predict_file", default="zh_tw_predict.xlsx", type=str)
+    parser.add_argument("--predict_file", default="th-TH_predict.xlsx", type=str)
     parser.add_argument(
         "--output_dir",
-        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\calling\886",
+        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\th-TH\th-TH\early_media_no_voice\bad_case",
         type=str,
     )
     args = parser.parse_args()
@@ -24,12 +24,16 @@ def main():
     args = get_args()
 
     output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
 
     df = pd.read_excel(args.predict_file)
     for i, row in df.iterrows():
         filename = row["filename"]
         ground_truth_ = row["ground_truth_"]
+        flag = row["flag"]
 
+        if flag == 1:
+            continue
         if ground_truth_ == "voicemail":
             shutil.copy(
                 filename,

examples/online_model_test/step_3_make_test.py

@@ -15,12 +15,12 @@ def get_args():
 
     parser.add_argument(
         "--src_dir",
-        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\calling\65\voicemail",
+        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\calling\63\voicemail",
         type=str,
     )
     parser.add_argument(
         "--tgt_dir",
-        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\voice_test_examples\65\95",
+        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\voice_test_examples\63\96",
         type=str,
     )
     parser.add_argument(

examples/online_model_test/test.py

@@ -0,0 +1,84 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+from pathlib import Path
+import shutil
+
+from gradio_client import Client, handle_file
+import librosa
+import pandas as pd
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--finished_dir",
+        default=r"D:\Users\tianx\HuggingSpaces\cc_audio_8\data\calling\66\wav_1ch",
+        type=str,
+    )
+    parser.add_argument(
+        "--src_dir",
+        default=r"D:/Users/tianx/HuggingDatasets/international_voice/data/sea-idn/audio_lib_hkg_1/audio_lib_hkg_1/th-TH/th-TH/",
+        type=str,
+    )
+    parser.add_argument(
+        "--tgt_dir",
+        default=r"D:\Users\tianx\HuggingDatasets\international_voice\data\sea-idn\audio_lib_hkg_1\audio_lib_hkg_1\th-TH\bad_case",
+        type=str,
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    finished_dir = Path(args.finished_dir)
+    src_dir = Path(args.src_dir)
+    tgt_dir = Path(args.tgt_dir)
+    tgt_dir.mkdir(parents=True, exist_ok=True)
+
+    # finished
+    finished = set()
+    for filename in finished_dir.glob("*.wav"):
+        splits = filename.stem.split("_")
+        call_id = splits[3]
+        if call_id in ("27521940-feef-4bfa-ba55-b1f00a10c64d",):
+            print(f"call_id: {call_id}")
+
+        finished.add(call_id)
+    print(f"finished count: {len(finished)}")
+
+    # call_id_to_wav_file_list
+    call_id_to_wav_file_list = defaultdict(list)
+    for filename in src_dir.glob("**/*.wav"):
+        splits = filename.stem.split("_")
+        call_id = splits[3]
+        language = splits[4]
+        scene_id = splits[5]
+        if call_id in ("27521940-feef-4bfa-ba55-b1f00a10c64d",):
+            print(f"call_id: {call_id}")
+
+        call_id_to_wav_file_list[call_id].append(filename.as_posix())
+    print(f"src count: {len(call_id_to_wav_file_list)}")
+
+    for filename in tqdm(src_dir.glob("**/active_media_r_*.wav")):
+        splits = filename.stem.split("_")
+        call_id = splits[3]
+        if call_id in ("27521940-feef-4bfa-ba55-b1f00a10c64d",):
+            print(f"call_id: {call_id}")
+
+        if call_id in finished:
+            wav_file_list = call_id_to_wav_file_list[call_id]
+            for wav_file in wav_file_list:
+                shutil.move(
+                    wav_file,
+                    tgt_dir.as_posix(),
+                )
+    return
+
+
+if __name__ == "__main__":
+    main()

examples/{vm_sound_classification → sound_classification_by_cnn}/run.sh

@@ -2,22 +2,22 @@
 
 : <<'END'
 
-sh run.sh --stage 0 --stop_stage 1 --system_version windows --file_folder_name file_dir --final_model_name sound-4-ch32 \
+sh run.sh --stage 0 --stop_stage 1 --system_version windows --file_folder_name file_dir --final_model_name sound-4-ch32-cnn \
 --filename_patterns "E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
 E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/id-ID/wav_finished/*/*.wav" \
 --label_plan 4
 
-sh run.sh --stage 2 --stop_stage 2 --system_version windows --file_folder_name file_dir --final_model_name sound-2-ch32 \
+sh run.sh --stage 2 --stop_stage 2 --system_version windows --file_folder_name file_dir --final_model_name sound-2-ch32-cnn \
 --filename_patterns "E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
 E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/id-ID/wav_finished/*/*.wav" \
 --label_plan 4
 
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 --label_plan 3 \
 --config_file "yaml/conv2d-classifier-3-ch4.yaml"
 
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ms-MY/wav_finished/*/*.wav" \
 --label_plan 2-voicemail \
 --config_file "yaml/conv2d-classifier-2-ch32.yaml"

examples/{vm_sound_classification → sound_classification_by_cnn}/run_batch.sh

@@ -3,25 +3,25 @@
 
 # sound ch4
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 2 \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 3 \
 #--config_file "yaml/conv2d-classifier-3-ch4.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 4 \
 #--config_file "yaml/conv2d-classifier-4-ch4.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 8 \
 #--config_file "yaml/conv2d-classifier-8-ch4.yaml"
@@ -29,25 +29,25 @@
 
 # sound ch8
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch8 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch8-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 2 \
 #--config_file "yaml/conv2d-classifier-2-ch8.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch8 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch8-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 3 \
 #--config_file "yaml/conv2d-classifier-3-ch8.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch8 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch8-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 4 \
 #--config_file "yaml/conv2d-classifier-4-ch8.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch8 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch8-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 8 \
 #--config_file "yaml/conv2d-classifier-8-ch8.yaml"
@@ -55,25 +55,25 @@
 
 # sound ch16
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch16 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch16-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 2 \
 #--config_file "yaml/conv2d-classifier-2-ch16.yaml"
 
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch16 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch16-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 3 \
 #--config_file "yaml/conv2d-classifier-3-ch16.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch16 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch16-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 4 \
 #--config_file "yaml/conv2d-classifier-4-ch16.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch16 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch16-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 8 \
 #--config_file "yaml/conv2d-classifier-8-ch16.yaml"
@@ -81,25 +81,25 @@
 
 # sound ch32
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 2 \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 3 \
 #--config_file "yaml/conv2d-classifier-3-ch32.yaml"
 #
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-4-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 4 \
 #--config_file "yaml/conv2d-classifier-4-ch32.yaml"
 
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-8-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 #--label_plan 8 \
 #--config_file "yaml/conv2d-classifier-8-ch32.yaml"
@@ -107,12 +107,12 @@
 
 # pretrained voicemail
 
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-2-ch4 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-2-ch4-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 --label_plan 2-voicemail \
 --config_file "yaml/conv2d-classifier-2-ch4.yaml"
 
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-2-ch32 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-2-ch32-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
 --label_plan 2-voicemail \
 --config_file "yaml/conv2d-classifier-2-ch32.yaml"
@@ -120,149 +120,149 @@ sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name fi
 
 # voicemail ch4
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-ph-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-ph-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-PH/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-sg-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-sg-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-SG/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-us-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-us-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-US/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-mx-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-mx-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/es-MX/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-pe-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-pe-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/es-PE/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-id-id-2-ch4 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-id-id-2-ch4-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/id-ID/wav_finished/*/*.wav" \
 --label_plan 2-voicemail \
 --config_file "yaml/conv2d-classifier-2-ch4.yaml" \
---pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ja-jp-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ja-jp-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ja-JP/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ko-kr-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ko-kr-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ko-KR/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ms-MY/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-pt-br-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-pt-br-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/pt-BR/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-th-th-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-th-th-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/th-TH/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-zh-tw-2-ch4 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-zh-tw-2-ch4-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/zh-TW/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch4.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch4-cnn.zip"
 
 
 # voicemail ch32
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-ph-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-ph-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-PH/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-sg-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-sg-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-SG/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-us-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-en-us-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/en-US/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-mx-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-mx-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/es-MX/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-pe-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-es-pe-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/es-PE/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-id-id-2-ch32 \
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-id-id-2-ch32-cnn \
 --filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/id-ID/wav_finished/*/*.wav" \
 --label_plan 2-voicemail \
 --config_file "yaml/conv2d-classifier-2-ch32.yaml" \
---pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ja-jp-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ja-jp-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ja-JP/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ko-kr-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ko-kr-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ko-KR/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ms-MY/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-pt-br-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-pt-br-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/pt-BR/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-th-th-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-th-th-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/th-TH/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"
 #
-#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-zh-tw-2-ch32 \
+#sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-zh-tw-2-ch32-cnn \
 #--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/zh-TW/wav_finished/*/*.wav" \
 #--label_plan 2-voicemail \
 #--config_file "yaml/conv2d-classifier-2-ch32.yaml" \
-#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32.zip"
+#--pretrained_model "/data/tianxing/PycharmProjects/cc_audio_8/trained_models/voicemail-2-ch32-cnn.zip"

examples/{vm_sound_classification → sound_classification_by_cnn}/step_3_train_model.py

@@ -50,7 +50,7 @@ def get_args():
     parser.add_argument("--config_file", default="conv2d_classifier.yaml", type=str)
     parser.add_argument(
         "--pretrained_model",
-        # default=(project_path / "trained_models/voicemail-en-sg-2-ch4.zip").as_posix(),
+        # default=(project_path / "trained_models/voicemail-en-sg-2-ch4-cnn.zip").as_posix(),
         default="null",
         type=str
     )

examples/sound_classification_by_lstm/run.sh

@@ -0,0 +1,197 @@
+#!/usr/bin/env bash
+
+: <<'END'
+
+sh run.sh --stage 0 --stop_stage 1 --system_version windows --file_folder_name file_dir --final_model_name sound-4-ch32-lstm \
+--filename_patterns "E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
+E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/id-ID/wav_finished/*/*.wav" \
+--label_plan 4
+
+sh run.sh --stage 2 --stop_stage 2 --system_version windows --file_folder_name file_dir --final_model_name sound-2-ch32-lstm \
+--filename_patterns "E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/wav_finished/en-US/wav_finished/*/*.wav \
+E:/Users/tianx/HuggingDatasets/cc_audio_8/data/wav_finished/id-ID/wav_finished/*/*.wav" \
+--label_plan 4
+
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name sound-3-ch32-lstm \
+--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav" \
+--label_plan 3 \
+--config_file "yaml/lstm_classifier-3-ch64.yaml"
+
+sh run.sh --stage 0 --stop_stage 6 --system_version centos --file_folder_name file_dir --final_model_name voicemail-ms-my-2-ch32-lstm \
+--filename_patterns "/data/tianxing/PycharmProjects/datasets/voicemail/ms-MY/wav_finished/*/*.wav" \
+--label_plan 2-voicemail \
+--config_file "yaml/lstm_classifier-2-ch64.yaml"
+
+END
+
+
+# params
+system_version="windows";
+verbose=true;
+stage=0 # start from 0 if you need to start from data preparation
+stop_stage=9
+
+work_dir="$(pwd)"
+file_folder_name=file_folder_name
+final_model_name=final_model_name
+filename_patterns="/data/tianxing/PycharmProjects/datasets/voicemail/*/wav_finished/*/*.wav"
+label_plan=4
+config_file="yaml/lstm_classifier-4-ch64.yaml"
+pretrained_model=null
+nohup_name=nohup.out
+
+country=en-US
+
+# model params
+batch_size=64
+max_epochs=200
+save_top_k=10
+patience=5
+
+
+# parse options
+while true; do
+  [ -z "${1:-}" ] && break;  # break if there are no arguments
+  case "$1" in
+    --*) name=$(echo "$1" | sed s/^--// | sed s/-/_/g);
+      eval '[ -z "${'"$name"'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
+      old_value="(eval echo \\$$name)";
+      if [ "${old_value}" == "true" ] || [ "${old_value}" == "false" ]; then
+        was_bool=true;
+      else
+        was_bool=false;
+      fi
+
+      # Set the variable to the right value-- the escaped quotes make it work if
+      # the option had spaces, like --cmd "queue.pl -sync y"
+      eval "${name}=\"$2\"";
+
+      # Check that Boolean-valued arguments are really Boolean.
+      if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
+        echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
+        exit 1;
+      fi
+      shift 2;
+      ;;
+
+    *) break;
+  esac
+done
+
+file_dir="${work_dir}/${file_folder_name}"
+final_model_dir="${work_dir}/../../trained_models/${final_model_name}";
+
+dataset="${file_dir}/dataset.xlsx"
+train_dataset="${file_dir}/train.xlsx"
+valid_dataset="${file_dir}/valid.xlsx"
+evaluation_file="${file_dir}/evaluation.xlsx"
+vocabulary_dir="${file_dir}/vocabulary"
+
+$verbose && echo "system_version: ${system_version}"
+$verbose && echo "file_folder_name: ${file_folder_name}"
+
+if [ $system_version == "windows" ]; then
+  alias python3='D:/Users/tianx/PycharmProjects/virtualenv/cc_audio_8/Scripts/python.exe'
+elif [ $system_version == "centos" ] || [ $system_version == "ubuntu" ]; then
+  #source /data/local/bin/cc_audio_8/bin/activate
+  alias python3='/data/local/bin/cc_audio_8/bin/python3'
+fi
+
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+  $verbose && echo "stage 0: prepare data"
+  cd "${work_dir}" || exit 1
+  python3 step_1_prepare_data.py \
+  --file_dir "${file_dir}" \
+  --filename_patterns "${filename_patterns}" \
+  --train_dataset "${train_dataset}" \
+  --valid_dataset "${valid_dataset}" \
+  --label_plan "${label_plan}" \
+
+fi
+
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+  $verbose && echo "stage 1: make vocabulary"
+  cd "${work_dir}" || exit 1
+  python3 step_2_make_vocabulary.py \
+  --vocabulary_dir "${vocabulary_dir}" \
+  --train_dataset "${train_dataset}" \
+  --valid_dataset "${valid_dataset}" \
+
+fi
+
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+  $verbose && echo "stage 2: train model"
+  cd "${work_dir}" || exit 1
+  python3 step_3_train_model.py \
+  --vocabulary_dir "${vocabulary_dir}" \
+  --train_dataset "${train_dataset}" \
+  --valid_dataset "${valid_dataset}" \
+  --serialization_dir "${file_dir}" \
+  --config_file "${config_file}" \
+  --pretrained_model "${pretrained_model}" \
+
+fi
+
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  $verbose && echo "stage 3: test model"
+  cd "${work_dir}" || exit 1
+  python3 step_4_evaluation_model.py \
+  --dataset "${dataset}" \
+  --vocabulary_dir "${vocabulary_dir}" \
+  --model_dir "${file_dir}/best" \
+  --output_file "${evaluation_file}" \
+
+fi
+
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  $verbose && echo "stage 4: export model"
+  cd "${work_dir}" || exit 1
+  python3 step_5_export_models.py \
+  --vocabulary_dir "${vocabulary_dir}" \
+  --model_dir "${file_dir}/best" \
+  --serialization_dir "${file_dir}" \
+
+fi
+
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+  $verbose && echo "stage 5: collect files"
+  cd "${work_dir}" || exit 1
+
+  mkdir -p ${final_model_dir}
+
+  cp "${file_dir}/best"/* "${final_model_dir}"
+  cp -r "${file_dir}/vocabulary" "${final_model_dir}"
+
+  cp "${file_dir}/evaluation.xlsx" "${final_model_dir}/evaluation.xlsx"
+
+  cp "${file_dir}/trace_model.zip" "${final_model_dir}/trace_model.zip"
+  cp "${file_dir}/trace_quant_model.zip" "${final_model_dir}/trace_quant_model.zip"
+  cp "${file_dir}/script_model.zip" "${final_model_dir}/script_model.zip"
+  cp "${file_dir}/script_quant_model.zip" "${final_model_dir}/script_quant_model.zip"
+
+  cd "${final_model_dir}/.." || exit 1;
+
+  if [ -e "${final_model_name}.zip" ]; then
+    rm -rf "${final_model_name}_backup.zip"
+    mv "${final_model_name}.zip" "${final_model_name}_backup.zip"
+  fi
+
+  zip -r "${final_model_name}.zip" "${final_model_name}"
+  rm -rf "${final_model_name}"
+
+fi
+
+
+if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
+  $verbose && echo "stage 6: clear file_dir"
+  cd "${work_dir}" || exit 1
+
+  rm -rf "${file_dir}";
+
+fi

examples/sound_classification_by_lstm/step_1_prepare_data.py

@@ -0,0 +1,193 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from glob import glob
+import os
+from pathlib import Path
+import random
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, "../../"))
+
+import pandas as pd
+from scipy.io import wavfile
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--file_dir", default="./", type=str)
+    parser.add_argument("--filename_patterns", type=str)
+
+    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
+    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)
+
+    parser.add_argument("--label_plan", default="4", type=str)
+
+    args = parser.parse_args()
+    return args
+
+
+def get_dataset(args):
+    filename_patterns = args.filename_patterns
+    filename_patterns = filename_patterns.split(" ")
+    print(filename_patterns)
+
+    file_dir = Path(args.file_dir)
+    file_dir.mkdir(exist_ok=True)
+
+    if args.label_plan == "2-voicemail":
+        label_map = {
+            "bell": "voicemail",
+            "white_noise": "non_voicemail",
+            "low_white_noise": "non_voicemail",
+            "high_white_noise": "non_voicemail",
+            # "music": "non_voicemail",
+            "mute": "non_voicemail",
+            "noise": "non_voicemail",
+            "noise_mute": "non_voicemail",
+            "voice": "non_voicemail",
+            "voicemail": "voicemail",
+        }
+    elif args.label_plan == "2":
+        label_map = {
+            "bell": "non_voice",
+            "white_noise": "non_voice",
+            "low_white_noise": "non_voice",
+            "high_white_noise": "non_voice",
+            "music": "non_voice",
+            "mute": "non_voice",
+            "noise": "non_voice",
+            "noise_mute": "non_voice",
+            "voice": "voice",
+            "voicemail": "voice",
+        }
+    elif args.label_plan == "3":
+        label_map = {
+            "bell": "voicemail",
+            "white_noise": "mute",
+            "low_white_noise": "mute",
+            "high_white_noise": "mute",
+            # "music": "music",
+            "mute": "mute",
+            "noise": "voice_or_noise",
+            "noise_mute": "voice_or_noise",
+            "voice": "voice_or_noise",
+            "voicemail": "voicemail",
+        }
+    elif args.label_plan == "4":
+        label_map = {
+            "bell": "voicemail",
+            "white_noise": "mute",
+            "low_white_noise": "mute",
+            "high_white_noise": "mute",
+            # "music": "music",
+            "mute": "mute",
+            "noise": "noise",
+            "noise_mute": "noise",
+            "voice": "voice",
+            "voicemail": "voicemail",
+        }
+    elif args.label_plan == "8":
+        label_map = {
+            "bell": "bell",
+            "white_noise": "white_noise",
+            "low_white_noise": "white_noise",
+            "high_white_noise": "white_noise",
+            "music": "music",
+            "mute": "mute",
+            "noise": "noise",
+            "noise_mute": "noise_mute",
+            "voice": "voice",
+            "voicemail": "voicemail",
+        }
+    else:
+        raise AssertionError
+
+    result = list()
+    for filename_pattern in filename_patterns:
+        filename_list = glob(filename_pattern)
+        for filename in tqdm(filename_list):
+            filename = Path(filename)
+            sample_rate, signal = wavfile.read(filename.as_posix())
+            if len(signal) < sample_rate * 2:
+                continue
+
+            folder = filename.parts[-2]
+            country = filename.parts[-4]
+
+            if folder not in label_map.keys():
+                continue
+
+            labels = label_map[folder]
+
+            random1 = random.random()
+            random2 = random.random()
+
+            result.append({
+                "filename": filename,
+                "folder": folder,
+                "category": country,
+                "labels": labels,
+                "random1": random1,
+                "random2": random2,
+                "flag": "TRAIN" if random2 < 0.8 else "TEST",
+            })
+
+    df = pd.DataFrame(result)
+    pivot_table = pd.pivot_table(df, index=["labels"], values=["filename"], aggfunc="count")
+    print(pivot_table)
+
+    df = df.sort_values(by=["random1"], ascending=False)
+    df.to_excel(
+        file_dir / "dataset.xlsx",
+        index=False,
+        # encoding="utf_8_sig"
+    )
+
+    return
+
+
+def split_dataset(args):
+    """分割训练集, 测试集"""
+    file_dir = Path(args.file_dir)
+    file_dir.mkdir(exist_ok=True)
+
+    df = pd.read_excel(file_dir / "dataset.xlsx")
+
+    train = list()
+    test = list()
+
+    for i, row in df.iterrows():
+        flag = row["flag"]
+        if flag == "TRAIN":
+            train.append(row)
+        else:
+            test.append(row)
+
+    train = pd.DataFrame(train)
+    train.to_excel(
+        args.train_dataset,
+        index=False,
+        # encoding="utf_8_sig"
+    )
+    test = pd.DataFrame(test)
+    test.to_excel(
+        args.valid_dataset,
+        index=False,
+        # encoding="utf_8_sig"
+    )
+
+    return
+
+
+def main():
+    args = get_args()
+    get_dataset(args)
+    split_dataset(args)
+    return
+
+
+if __name__ == "__main__":
+    main()

examples/sound_classification_by_lstm/step_2_make_vocabulary.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+import os
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, "../../"))
+
+import pandas as pd
+
+from toolbox.torch.utils.data.vocabulary import Vocabulary
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--vocabulary_dir", default="vocabulary", type=str)
+
+    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
+    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    train_dataset = pd.read_excel(args.train_dataset)
+    valid_dataset = pd.read_excel(args.valid_dataset)
+
+    vocabulary = Vocabulary()
+
+    # train
+    for i, row in train_dataset.iterrows():
+        label = row["labels"]
+        vocabulary.add_token_to_namespace(label, namespace="labels")
+
+    # valid
+    for i, row in valid_dataset.iterrows():
+        label = row["labels"]
+        vocabulary.add_token_to_namespace(label, namespace="labels")
+
+    vocabulary.save_to_files(args.vocabulary_dir)
+
+    return
+
+
+if __name__ == "__main__":
+    main()

examples/sound_classification_by_lstm/step_3_train_model.py

@@ -0,0 +1,367 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import logging
+from logging.handlers import TimedRotatingFileHandler
+import os
+import platform
+from pathlib import Path
+import random
+import sys
+import shutil
+import tempfile
+from typing import List
+import zipfile
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, "../../"))
+
+import numpy as np
+import torch
+from torch.utils.data.dataloader import DataLoader
+from tqdm import tqdm
+
+from toolbox.torch.modules.loss import FocalLoss, HingeLoss, HingeLinear
+from toolbox.torch.training.metrics.categorical_accuracy import CategoricalAccuracy
+from toolbox.torch.utils.data.vocabulary import Vocabulary
+from toolbox.torch.utils.data.dataset.wave_classifier_excel_dataset import WaveClassifierExcelDataset
+from toolbox.torchaudio.models.lstm_audio_classifier.modeling_lstm_audio_classifier import LSTMClassifierPretrainedModel
+from toolbox.torchaudio.models.lstm_audio_classifier.configuration_lstm_audio_classifier import LSTMClassifierConfig
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--vocabulary_dir", default="vocabulary", type=str)
+
+    parser.add_argument("--train_dataset", default="train.xlsx", type=str)
+    parser.add_argument("--valid_dataset", default="valid.xlsx", type=str)
+
+    parser.add_argument("--max_epochs", default=100, type=int)
+
+    parser.add_argument("--batch_size", default=64, type=int)
+    parser.add_argument("--learning_rate", default=1e-3, type=float)
+    parser.add_argument("--num_serialized_models_to_keep", default=10, type=int)
+    parser.add_argument("--patience", default=5, type=int)
+    parser.add_argument("--serialization_dir", default="serialization_dir", type=str)
+    parser.add_argument("--seed", default=0, type=int)
+
+    parser.add_argument("--config_file", default="conv2d_classifier.yaml", type=str)
+    parser.add_argument(
+        "--pretrained_model",
+        # default=(project_path / "trained_models/voicemail-en-sg-2-ch4-cnn.zip").as_posix(),
+        default="null",
+        type=str
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def logging_config(file_dir: str):
+    fmt = "%(asctime)s - %(name)s - %(levelname)s  %(filename)s:%(lineno)d >  %(message)s"
+
+    logging.basicConfig(format=fmt,
+                        datefmt="%m/%d/%Y %H:%M:%S",
+                        level=logging.DEBUG)
+    file_handler = TimedRotatingFileHandler(
+        filename=os.path.join(file_dir, "main.log"),
+        encoding="utf-8",
+        when="D",
+        interval=1,
+        backupCount=7
+    )
+    file_handler.setLevel(logging.INFO)
+    file_handler.setFormatter(logging.Formatter(fmt))
+    logger = logging.getLogger(__name__)
+    logger.addHandler(file_handler)
+
+    return logger
+
+
+class CollateFunction(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, batch: List[dict]):
+        array_list = list()
+        label_list = list()
+        for sample in batch:
+            array = sample["waveform"]
+            label = sample["label"]
+
+            l = len(array)
+            if l < 16000:
+                delta = int(16000 - l)
+                array = np.concatenate([array, np.zeros(shape=(delta,), dtype=np.float32)], axis=-1)
+            if l > 16000:
+                array = array[:16000]
+
+            array_list.append(array)
+            label_list.append(label)
+
+        array_list = torch.stack(array_list)
+        label_list = torch.stack(label_list)
+        return array_list, label_list
+
+
+collate_fn = CollateFunction()
+
+
+def main():
+    args = get_args()
+
+    serialization_dir = Path(args.serialization_dir)
+    serialization_dir.mkdir(parents=True, exist_ok=True)
+
+    logger = logging_config(serialization_dir)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    logger.info("set seed: {}".format(args.seed))
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    n_gpu = torch.cuda.device_count()
+    logger.info("GPU available count: {}; device: {}".format(n_gpu, device))
+
+    vocabulary = Vocabulary.from_files(args.vocabulary_dir)
+
+    # datasets
+    logger.info("prepare datasets")
+    train_dataset = WaveClassifierExcelDataset(
+        vocab=vocabulary,
+        excel_file=args.train_dataset,
+        category=None,
+        category_field="category",
+        label_field="labels",
+        expected_sample_rate=8000,
+        max_wave_value=32768.0,
+    )
+    valid_dataset = WaveClassifierExcelDataset(
+        vocab=vocabulary,
+        excel_file=args.valid_dataset,
+        category=None,
+        category_field="category",
+        label_field="labels",
+        expected_sample_rate=8000,
+        max_wave_value=32768.0,
+    )
+    train_data_loader = DataLoader(
+        dataset=train_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        # Linux 系统中可以使用多个子进程���载数据, 而在 Windows 系统中不能.
+        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
+        collate_fn=collate_fn,
+        pin_memory=False,
+        # prefetch_factor=64,
+    )
+    valid_data_loader = DataLoader(
+        dataset=valid_dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        # Linux 系统中可以使用多个子进程加载数据, 而在 Windows 系统中不能.
+        num_workers=0 if platform.system() == "Windows" else os.cpu_count() // 2,
+        collate_fn=collate_fn,
+        pin_memory=False,
+        # prefetch_factor=64,
+    )
+
+    # models
+    logger.info(f"prepare models. config_file: {args.config_file}")
+    config = LSTMClassifierConfig.from_pretrained(
+        pretrained_model_name_or_path=args.config_file,
+        # num_labels=vocabulary.get_vocab_size(namespace="labels")
+    )
+    if not config.cls_head_param["num_labels"] == vocabulary.get_vocab_size(namespace="labels"):
+        raise AssertionError("expected num labels: {} instead of {}.".format(
+            vocabulary.get_vocab_size(namespace="labels"),
+            config.cls_head_param["num_labels"],
+        ))
+    model = LSTMClassifierPretrainedModel(
+        config=config,
+    )
+
+    if args.pretrained_model is not None and os.path.exists(args.pretrained_model):
+        logger.info(f"load pretrained model state dict from: {args.pretrained_model}")
+        pretrained_model = Path(args.pretrained_model)
+        with zipfile.ZipFile(pretrained_model.as_posix(), "r") as f_zip:
+            out_root = Path(tempfile.gettempdir()) / "cc_audio_8"
+            # print(out_root.as_posix())
+            if out_root.exists():
+                shutil.rmtree(out_root.as_posix())
+            out_root.mkdir(parents=True, exist_ok=True)
+            f_zip.extractall(path=out_root)
+
+        tgt_path = out_root / pretrained_model.stem
+        model_pt_file = tgt_path / "model.pt"
+        with open(model_pt_file, "rb") as f:
+            state_dict = torch.load(f, map_location="cpu")
+        model.load_state_dict(state_dict=state_dict)
+
+    model.to(device)
+    model.train()
+
+    # optimizer
+    logger.info("prepare optimizer, lr_scheduler, loss_fn, categorical_accuracy")
+    param_optimizer = model.parameters()
+    optimizer = torch.optim.Adam(
+        param_optimizer,
+        lr=args.learning_rate,
+    )
+    # lr_scheduler = torch.optim.lr_scheduler.StepLR(
+    #     optimizer,
+    #     step_size=2000
+    # )
+    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+        optimizer,
+        milestones=[10000, 20000, 30000, 40000, 50000], gamma=0.5
+    )
+    focal_loss = FocalLoss(
+        num_classes=vocabulary.get_vocab_size(namespace="labels"),
+        reduction="mean",
+    )
+    categorical_accuracy = CategoricalAccuracy()
+
+    # training loop
+    logger.info("training")
+
+    training_loss = 10000000000
+    training_accuracy = 0.
+    evaluation_loss = 10000000000
+    evaluation_accuracy = 0.
+
+    model_list = list()
+    best_idx_epoch = None
+    best_accuracy = None
+    patience_count = 0
+
+    for idx_epoch in range(args.max_epochs):
+        categorical_accuracy.reset()
+        total_loss = 0.
+        total_examples = 0.
+        progress_bar = tqdm(
+            total=len(train_data_loader),
+            desc="Training; epoch: {}".format(idx_epoch),
+        )
+        for batch in train_data_loader:
+            input_ids, label_ids = batch
+            input_ids = input_ids.to(device)
+            label_ids: torch.LongTensor = label_ids.to(device).long()
+
+            logits = model.forward(input_ids)
+            loss = focal_loss.forward(logits, label_ids.view(-1))
+            categorical_accuracy(logits, label_ids)
+
+            total_loss += loss.item()
+            total_examples += input_ids.size(0)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+
+            training_loss = total_loss / total_examples
+            training_loss = round(training_loss, 4)
+            training_accuracy = categorical_accuracy.get_metric()["accuracy"]
+            training_accuracy = round(training_accuracy, 4)
+
+            progress_bar.update(1)
+            progress_bar.set_postfix({
+                "training_loss": training_loss,
+                "training_accuracy": training_accuracy,
+            })
+
+        categorical_accuracy.reset()
+        total_loss = 0.
+        total_examples = 0.
+        progress_bar = tqdm(
+            total=len(valid_data_loader),
+            desc="Evaluation; epoch: {}".format(idx_epoch),
+        )
+        for batch in valid_data_loader:
+            input_ids, label_ids = batch
+            input_ids = input_ids.to(device)
+            label_ids: torch.LongTensor = label_ids.to(device).long()
+
+            with torch.no_grad():
+                logits = model.forward(input_ids)
+                loss = focal_loss.forward(logits, label_ids.view(-1))
+                categorical_accuracy(logits, label_ids)
+
+            total_loss += loss.item()
+            total_examples += input_ids.size(0)
+
+            evaluation_loss = total_loss / total_examples
+            evaluation_loss = round(evaluation_loss, 4)
+            evaluation_accuracy = categorical_accuracy.get_metric()["accuracy"]
+            evaluation_accuracy = round(evaluation_accuracy, 4)
+
+            progress_bar.update(1)
+            progress_bar.set_postfix({
+                "evaluation_loss": evaluation_loss,
+                "evaluation_accuracy": evaluation_accuracy,
+            })
+
+        # save path
+        epoch_dir = serialization_dir / "epoch-{}".format(idx_epoch)
+        epoch_dir.mkdir(parents=True, exist_ok=False)
+
+        # save models
+        model.save_pretrained(epoch_dir.as_posix())
+
+        model_list.append(epoch_dir)
+        if len(model_list) >= args.num_serialized_models_to_keep:
+            model_to_delete: Path = model_list.pop(0)
+            shutil.rmtree(model_to_delete.as_posix())
+
+        # save metric
+        if best_accuracy is None:
+            best_idx_epoch = idx_epoch
+            best_accuracy = evaluation_accuracy
+        elif evaluation_accuracy > best_accuracy:
+            best_idx_epoch = idx_epoch
+            best_accuracy = evaluation_accuracy
+        else:
+            pass
+
+        metrics = {
+            "idx_epoch": idx_epoch,
+            "best_idx_epoch": best_idx_epoch,
+            "best_accuracy": best_accuracy,
+            "training_loss": training_loss,
+            "training_accuracy": training_accuracy,
+            "evaluation_loss": evaluation_loss,
+            "evaluation_accuracy": evaluation_accuracy,
+            "learning_rate": optimizer.param_groups[0]['lr'],
+        }
+        metrics_filename = epoch_dir / "metrics_epoch.json"
+        with open(metrics_filename, "w", encoding="utf-8") as f:
+            json.dump(metrics, f, indent=4, ensure_ascii=False)
+
+        # save best
+        best_dir = serialization_dir / "best"
+        if best_idx_epoch == idx_epoch:
+            if best_dir.exists():
+                shutil.rmtree(best_dir)
+            shutil.copytree(epoch_dir, best_dir)
+
+        # early stop
+        early_stop_flag = False
+        if best_idx_epoch == idx_epoch:
+            patience_count = 0
+        else:
+            patience_count += 1
+        if patience_count >= args.patience:
+            early_stop_flag = True
+
+        # early stop
+        if early_stop_flag:
+            break
+    return
+
+
+if __name__ == "__main__":
+    main()

examples/sound_classification_by_lstm/yaml/lstm_classifier-4-ch64.yaml

@@ -0,0 +1,27 @@
+model_name: "lstm_audio_classifier"
+
+mel_spectrogram_param:
+  sample_rate: 8000
+  n_fft: 512
+  win_length: 200
+  hop_length: 80
+  f_min: 10
+  f_max: 3800
+  window_fn: hamming
+  n_mels: 80
+
+lstm_layer_param:
+  input_size: 80
+  hidden_size: 64
+  num_layers: 3
+  dropout: 0.2
+  pool_layer: last
+
+cls_head_param:
+  input_dim: 64
+  num_layers: 1
+  hidden_dims:
+    - 32
+  activations: relu
+  dropout: 0.1
+  num_labels: 4