feat(engine): 替换重采样模型、SOSV 添加标点恢复模型

- 将 samplerate 库替换为 resampy 库，提高重采样质量
- Shepra-ONNX SenseVoice 添加中文和英语标点恢复模型

engine/utils/__init__.py

@@ -1,9 +1,4 @@
-from .audioprcs import (
-    merge_chunk_channels,
-    resample_chunk_mono,
-    resample_chunk_mono_np,
-    resample_mono_chunk
-)
+from .audioprcs import merge_chunk_channels, resample_chunk_mono
 from .sysout import stdout, stdout_err, stdout_cmd, stdout_obj, stderr
 from .shared import shared_data
 from .server import start_server

engine/utils/audioprcs.py

@@ -1,4 +1,4 @@
-import samplerate
+import resampy
 import numpy as np
 import numpy.core.multiarray # do not remove
 
@@ -24,16 +24,15 @@ def merge_chunk_channels(chunk: bytes, channels: int) -> bytes:
     return chunk_mono.tobytes()
 
 
-def resample_chunk_mono(chunk: bytes, channels: int, orig_sr: int, target_sr: int, mode="sinc_best") -> bytes:
+def resample_chunk_mono(chunk: bytes, channels: int, orig_sr: int, target_sr: int) -> bytes:
     """
-    将当前多通道音频数据块转换成单通道音频数据块，然后进行重采样
+    将当前多通道音频数据块转换成单通道音频数据块，并进行重采样
 
     Args:
         chunk: 多通道音频数据块
         channels: 通道数
         orig_sr: 原始采样率
         target_sr: 目标采样率
-        mode: 重采样模式，可选：'sinc_best' | 'sinc_medium' | 'sinc_fastest' | 'zero_order_hold' | 'linear'
 
     Return:
         单通道音频数据块
@@ -52,82 +51,14 @@ def resample_chunk_mono(chunk: bytes, channels: int, orig_sr: int, target_sr: in
     if orig_sr == target_sr:
         return chunk_mono.astype(np.int16).tobytes()
     
-    ratio = target_sr / orig_sr
-    chunk_mono_r = samplerate.resample(chunk_mono, ratio, converter_type=mode)
+    chunk_mono_r = resampy.resample(chunk_mono, orig_sr, target_sr)
     chunk_mono_r = np.round(chunk_mono_r).astype(np.int16)
-    real_len = round(chunk_mono.shape[0] * ratio)
+    real_len = round(chunk_mono.shape[0] * target_sr / orig_sr)
+    if(chunk_mono_r.shape[0] != real_len):
+        print(chunk_mono_r.shape[0], real_len)
     if(chunk_mono_r.shape[0] > real_len):
         chunk_mono_r = chunk_mono_r[:real_len]
     else:
         while chunk_mono_r.shape[0] < real_len:
             chunk_mono_r = np.append(chunk_mono_r, chunk_mono_r[-1])
     return chunk_mono_r.tobytes()
-
-
-def resample_chunk_mono_np(chunk: bytes, channels: int, orig_sr: int, target_sr: int, mode="sinc_best", dtype=np.float32) -> np.ndarray:
-    """
-    将当前多通道音频数据块转换成单通道音频数据块，然后进行重采样，返回 Numpy 数组
-
-    Args:
-        chunk: 多通道音频数据块
-        channels: 通道数
-        orig_sr: 原始采样率
-        target_sr: 目标采样率
-        mode: 重采样模式，可选：'sinc_best' | 'sinc_medium' | 'sinc_fastest' | 'zero_order_hold' | 'linear'
-        dtype: 返回 Numpy 数组的数据类型
-
-    Return:
-        单通道音频数据块
-    """
-    if channels == 1:
-        chunk_mono = np.frombuffer(chunk, dtype=np.int16)
-        chunk_mono = chunk_mono.astype(np.float32)
-    else:
-        # (length * channels,)
-        chunk_np = np.frombuffer(chunk, dtype=np.int16)
-        # (length, channels)
-        chunk_np = chunk_np.reshape(-1, channels)
-        # (length,)
-        chunk_mono = np.mean(chunk_np.astype(np.float32), axis=1)
-
-    if orig_sr == target_sr:
-        return chunk_mono.astype(dtype)
-
-    ratio = target_sr / orig_sr
-    chunk_mono_r = samplerate.resample(chunk_mono, ratio, converter_type=mode)
-    chunk_mono_r = chunk_mono_r.astype(dtype)
-    real_len = round(chunk_mono.shape[0] * ratio)
-    if(chunk_mono_r.shape[0] > real_len):
-        chunk_mono_r = chunk_mono_r[:real_len]
-    else:
-        while chunk_mono_r.shape[0] < real_len:
-            chunk_mono_r = np.append(chunk_mono_r, chunk_mono_r[-1])
-    return chunk_mono_r
-
-
-def resample_mono_chunk(chunk: bytes, orig_sr: int, target_sr: int, mode="sinc_best") -> bytes:
-    """
-    将当前单通道音频块进行重采样
-
-    Args:
-        chunk: 单通道音频数据块
-        orig_sr: 原始采样率
-        target_sr: 目标采样率
-        mode: 重采样模式，可选：'sinc_best' | 'sinc_medium' | 'sinc_fastest' | 'zero_order_hold' | 'linear'
-
-    Return:
-        单通道音频数据块
-    """
-    if orig_sr == target_sr: return chunk
-    chunk_np = np.frombuffer(chunk, dtype=np.int16)
-    chunk_np = chunk_np.astype(np.float32)
-    ratio = target_sr / orig_sr
-    chunk_r =  samplerate.resample(chunk_np, ratio, converter_type=mode)
-    chunk_r = np.round(chunk_r).astype(np.int16)
-    real_len = round(chunk_np.shape[0] * ratio)
-    if(chunk_r.shape[0] > real_len):
-        chunk_r = chunk_r[:real_len]
-    else:
-        while chunk_r.shape[0] < real_len:
-            chunk_r = np.append(chunk_r, chunk_r[-1])
-    return chunk_r.tobytes()

engine/utils/translation.py

@@ -13,6 +13,7 @@ lang_map = {
     'ru': 'Russian',
     'ja': 'Japanese',
     'ko': 'Korean',
+    'zh': 'Chinese',
     'zh-cn': 'Chinese'
 }