Revert "Upgrade to Silero-Vad V5 (SYSTRAN#884)"

This reverts commit 8d400e9.

faster_whisper/vad.py

@@ -1,6 +1,7 @@
 import bisect
 import functools
 import os
+import warnings
 
 from typing import List, NamedTuple, Optional
 
@@ -24,13 +25,17 @@ class VadOptions(NamedTuple):
         split aggressively just before max_speech_duration_s.
       min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
         before separating it
+      window_size_samples: Audio chunks of window_size_samples size are fed to the silero VAD model.
+        WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate.
+        Values other than these may affect model performance!!
       speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
     """
 
     threshold: float = 0.5
     min_speech_duration_ms: int = 250
     max_speech_duration_s: float = float("inf")
     min_silence_duration_ms: int = 2000
+    window_size_samples: int = 1024
     speech_pad_ms: int = 400
 
 
@@ -56,8 +61,15 @@ def get_speech_timestamps(
     min_speech_duration_ms = vad_options.min_speech_duration_ms
     max_speech_duration_s = vad_options.max_speech_duration_s
     min_silence_duration_ms = vad_options.min_silence_duration_ms
-    window_size_samples = 512
+    window_size_samples = vad_options.window_size_samples
     speech_pad_ms = vad_options.speech_pad_ms
+
+    if window_size_samples not in [512, 1024, 1536]:
+        warnings.warn(
+            "Unusual window_size_samples! Supported window_size_samples:\n"
+            " - [512, 1024, 1536] for 16000 sampling_rate"
+        )
+
     sampling_rate = 16000
     min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
     speech_pad_samples = sampling_rate * speech_pad_ms / 1000
@@ -72,14 +84,14 @@ def get_speech_timestamps(
     audio_length_samples = len(audio)
 
     model = get_vad_model()
-    state, context = model.get_initial_states(batch_size=1)
+    state = model.get_initial_state(batch_size=1)
 
     speech_probs = []
     for current_start_sample in range(0, audio_length_samples, window_size_samples):
         chunk = audio[current_start_sample : current_start_sample + window_size_samples]
         if len(chunk) < window_size_samples:
             chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
-        speech_prob, state, context = model(chunk, state, context, sampling_rate)
+        speech_prob, state = model(chunk, state, sampling_rate)
         speech_probs.append(speech_prob)
 
     triggered = False
@@ -249,12 +261,12 @@ def __init__(self, path):
             sess_options=opts,
         )
 
-    def get_initial_states(self, batch_size: int):
-        state = np.zeros((2, batch_size, 128), dtype=np.float32)
-        context = np.zeros((batch_size, 64), dtype=np.float32)
-        return state, context
+    def get_initial_state(self, batch_size: int):
+        h = np.zeros((2, batch_size, 64), dtype=np.float32)
+        c = np.zeros((2, batch_size, 64), dtype=np.float32)
+        return h, c
 
-    def __call__(self, x, state, context, sr: int):
+    def __call__(self, x, state, sr: int):
         if len(x.shape) == 1:
             x = np.expand_dims(x, 0)
         if len(x.shape) > 2:
@@ -264,15 +276,16 @@ def __call__(self, x, state, context, sr: int):
         if sr / x.shape[1] > 31.25:
             raise ValueError("Input audio chunk is too short")
 
-        x = np.concatenate([context, x], axis=1)
+        h, c = state
 
         ort_inputs = {
             "input": x,
-            "state": state,
+            "h": h,
+            "c": c,
             "sr": np.array(sr, dtype="int64"),
         }
 
-        out, state = self.session.run(None, ort_inputs)
-        context = x[..., -64:]
+        out, h, c = self.session.run(None, ort_inputs)
+        state = (h, c)
 
-        return out, state, context
+        return out, state