feat:add DeepFilterNet for speech enhancement to obtain clear speech input

VAD/vad_handler.py

+import torchaudio
 from VAD.vad_iterator import VADIterator
 from baseHandler import BaseHandler
 import numpy as np
@@ -5,7 +6,7 @@ import torch
 from rich.console import Console
 
 from utils.utils import int2float
-
+from df.enhance import enhance, init_df
 import logging
 
 logger = logging.getLogger(__name__)
@@ -28,6 +29,7 @@ class VADHandler(BaseHandler):
         min_speech_ms=500,
         max_speech_ms=float("inf"),
         speech_pad_ms=30,
+        audio_enhancement=True
     ):
         self.should_listen = should_listen
         self.sample_rate = sample_rate
@@ -42,6 +44,9 @@ class VADHandler(BaseHandler):
             min_silence_duration_ms=min_silence_ms,
             speech_pad_ms=speech_pad_ms,
         )
+        self.audio_enhancement = audio_enhancement
+        if audio_enhancement:
+            self.enhanced_model, self.df_state, _ = init_df()
 
     def process(self, audio_chunk):
         audio_int16 = np.frombuffer(audio_chunk, dtype=np.int16)
@@ -58,4 +63,13 @@ class VADHandler(BaseHandler):
             else:
                 self.should_listen.clear()
                 logger.debug("Stop listening")
+                if self.audio_enhancement:
+                    if self.sample_rate != self.df_state.sr():
+                        audio_float32 = torchaudio.functional.resample(torch.from_numpy(array),orig_freq=self.sample_rate,
+                                                                       new_freq=self.df_state.sr())
+                        enhanced = enhance(self.enhanced_model, self.df_state, audio_float32.unsqueeze(0))
+                        enhanced = torchaudio.functional.resample(enhanced, orig_freq=self.df_state.sr(),new_freq=self.sample_rate)
+                    else:
+                        enhanced = enhance(self.enhanced_model, self.df_state, audio_float32)
+                    array = enhanced.numpy().squeeze()
                 yield array

arguments_classes/vad_arguments.py

@@ -39,3 +39,9 @@ class VADHandlerArguments:
             "help": "Amount of padding added to the beginning and end of detected speech segments. Measured in milliseconds. Default is 250 ms."
         },
     )
+    audio_enhancement:bool = field(
+        default=True,
+        metadata={
+            "help": "improves sound quality by applying techniques like noise reduction, equalization, and echo cancellation. Default is True."
+        },
+    )

requirements.txt

@@ -4,4 +4,5 @@ melotts @ git+https://github.com/andimarafioti/MeloTTS.git#egg=MeloTTS  # made a
 torch==2.4.0
 sounddevice==0.5.0
 funasr
-modelscope
\ No newline at end of file
+modelscope
+deepfilternet

requirements_mac.txt

@@ -6,4 +6,5 @@ sounddevice==0.5.0
 lightning-whisper-mlx>=0.0.10
 mlx-lm>=0.14.0
 funasr>=1.1.6
-modelscope>=1.17.1
\ No newline at end of file
+modelscope>=1.17.1
+deepfilternet