Make VAD model in documentations ggml-silero-v6.2.0

Author: KitaitiMakoto

README.md

@@ -755,23 +755,23 @@ written in Python that is fast and accurate.
 
 Models can be downloaded by running the following command on Linux or MacOS:
 ```console
-$ ./models/download-vad-model.sh silero-v5.1.2
-Downloading ggml model silero-v5.1.2 from 'https://huggingface.co/ggml-org/whisper-vad' ...
-ggml-silero-v5.1.2.bin        100%[==============================================>] 864.35K  --.-KB/s    in 0.04s
-Done! Model 'silero-v5.1.2' saved in '/path/models/ggml-silero-v5.1.2.bin'
+$ ./models/download-vad-model.sh silero-v6.2.0
+Downloading ggml model silero-v6.2.0 from 'https://huggingface.co/ggml-org/whisper-vad' ...
+ggml-silero-v6.2.0.bin        100%[==============================================>] 864.35K  --.-KB/s    in 0.04s
+Done! Model 'silero-v6.2.0' saved in '/path/models/ggml-silero-v6.2.0.bin'
 You can now use it like this:
 
-  $ ./build/bin/whisper-cli -vm /path/models/ggml-silero-v5.1.2.bin --vad -f samples/jfk.wav -m models/ggml-base.en.bin
+  $ ./build/bin/whisper-cli -vm /path/models/ggml-silero-v6.2.0.bin --vad -f samples/jfk.wav -m models/ggml-base.en.bin
 
 ```
 And the following command on Windows:
 ```console
-> .\models\download-vad-model.cmd silero-v5.1.2
-Downloading vad model silero-v5.1.2...
-Done! Model silero-v5.1.2 saved in C:\Users\danie\work\ai\whisper.cpp\ggml-silero-v5.1.2.bin
+> .\models\download-vad-model.cmd silero-v6.2.0
+Downloading vad model silero-v6.2.0...
+Done! Model silero-v6.2.0 saved in C:\Users\danie\work\ai\whisper.cpp\ggml-silero-v6.2.0.bin
 You can now use it like this:
 
-C:\path\build\bin\Release\whisper-cli.exe -vm C:\path\ggml-silero-v5.1.2.bin --vad -m models/ggml-base.en.bin -f samples\jfk.wav
+C:\path\build\bin\Release\whisper-cli.exe -vm C:\path\ggml-silero-v6.2.0.bin --vad -m models/ggml-base.en.bin -f samples\jfk.wav
 
 ```
 
@@ -783,15 +783,15 @@ This model can be also be converted manually to ggml using the following command
 $ python3 -m venv venv && source venv/bin/activate
 $ (venv) pip install silero-vad
 $ (venv) $ python models/convert-silero-vad-to-ggml.py --output models/silero.bin
-Saving GGML Silero-VAD model to models/silero-v5.1.2-ggml.bin
+Saving GGML Silero-VAD model to models/silero-v6.2.0-ggml.bin
 ```
 And it can then be used with whisper as follows:
 ```console
 $ ./build/bin/whisper-cli \
    --file ./samples/jfk.wav \
    --model ./models/ggml-base.en.bin \
    --vad \
-   --vad-model ./models/silero-v5.1.2-ggml.bin
+   --vad-model ./models/silero-v6.2.0-ggml.bin
 ```
 
 ### VAD Options

bindings/ruby/README.md

@@ -134,20 +134,20 @@ Support for Voice Activity Detection (VAD) can be enabled by setting `Whisper::P
 ```ruby
 Whisper::Params.new(
   vad: true,
-  vad_model_path: "silero-v5.1.2",
+  vad_model_path: "silero-v6.2.0",
   # other arguments...
 )
 ```
 
-When you pass the model name (`"silero-v5.1.2"`) or URI (`https://huggingface.co/ggml-org/whisper-vad/resolve/main/ggml-silero-v5.1.2.bin`), it will be downloaded automatically.
-Currently, "silero-v5.1.2" is registered as pre-converted model like ASR models. You also specify file path or URI of model.
+When you pass the model name (`"silero-v6.2.0"`) or URI (`https://huggingface.co/ggml-org/whisper-vad/resolve/main/ggml-silero-v6.2.0.bin`), it will be downloaded automatically.
+Currently, "silero-v6.2.0" is registered as pre-converted model like ASR models. You also specify file path or URI of model.
 
 If you need configure VAD behavior, pass params for that:
 
 ```ruby
 Whisper::Params.new(
   vad: true,
-  vad_model_path: "silero-v5.1.2",
+  vad_model_path: "silero-v6.2.0",
   vad_params: Whisper::VAD::Params.new(
     threshold: 1.0, # defaults to 0.5
     min_speech_duration_ms: 500, # defaults to 250
@@ -330,7 +330,7 @@ Using VAD separately from ASR
 VAD feature itself is useful. You can use it separately from ASR:
 
 ```ruby
-vad = Whisper::VAD::Context.new("silero-v5.1.2")
+vad = Whisper::VAD::Context.new("silero-v6.2.0")
 vad
   .detect("path/to/audio.wav", Whisper::VAD::Params.new)
   .each_with_index do |segment, index|

examples/addon.node/README.md

@@ -54,7 +54,7 @@ Before using VAD, download a VAD model:
 
 ```shell
 # From the whisper.cpp root directory
-./models/download-vad-model.sh silero-v5.1.2
+./models/download-vad-model.sh silero-v6.2.0
 ```
 
 ### VAD Parameters
@@ -85,7 +85,7 @@ const vadParams = {
   model: path.join(__dirname, "../../models/ggml-base.en.bin"),
   fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
   vad: true,
-  vad_model: path.join(__dirname, "../../models/ggml-silero-v5.1.2.bin"),
+  vad_model: path.join(__dirname, "../../models/ggml-silero-v6.2.0.bin"),
   vad_threshold: 0.5,
   progress_callback: (progress) => console.log(`Progress: ${progress}%`)
 };

examples/vad-speech-segments/README.md

@@ -15,7 +15,7 @@ The examples can be run using the following command, which uses a model
 that we use internally for testing:
 ```console
 ./build/bin/vad-speech-segments \
-    -vad-model models/for-tests-silero-v5.1.2-ggml.bin \
+    -vad-model models/for-tests-silero-v6.2.0-ggml.bin \
     --file samples/jfk.wav \
     --no-prints
 

tests/earnings21/README.md

@@ -77,11 +77,11 @@ First, you need to download a VAD model:
 
 ```
 $ # Execute the commands below in the project root dir.
-$ ./models/download-vad-model.sh silero-v5.1.2
+$ ./models/download-vad-model.sh silero-v6.2.0
 ```
 
 Create `eval.conf` with the following content:
 
 ```
-WHISPER_FLAGS = --no-prints --language en --output-txt --vad --vad-model ../../models/ggml-silero-v5.1.2.bin
+WHISPER_FLAGS = --no-prints --language en --output-txt --vad --vad-model ../../models/ggml-silero-v6.2.0.bin
 ```