From 2582569bbe0cba057d4c02faba78be3fe806ef6f Mon Sep 17 00:00:00 2001 From: Evan King Date: Thu, 24 Oct 2024 14:44:27 -0500 Subject: [PATCH 1/7] Format documentation; add example --- moonshine/demo/README.md | 168 ++++++++++++------------------------- moonshine/demo/example.mp4 | Bin 0 -> 448017 bytes 2 files changed, 54 insertions(+), 114 deletions(-) create mode 100644 moonshine/demo/example.mp4 diff --git a/moonshine/demo/README.md b/moonshine/demo/README.md index 9b2e15b..f609fc1 100644 --- a/moonshine/demo/README.md +++ b/moonshine/demo/README.md @@ -1,101 +1,63 @@ -# Live caption microphone demo. - -This folder contains the demo Python script -[live_captions.py](/moonshine/demo/live_captions.py). -The script runs Moonshine model on segments of speech detected in the microphone -signal using a voice activity detector called -[silero-vad](https://github.com/snakers4/silero-vad). The script prints -scrolling text or "live captions" assembled from the model predictions. - -The following steps were tested in `uv` virtual environment v0.4.25 created in -Ubuntu 22.04 and Ubuntu 24.04 home folders running on a MacBook Pro M2 (ARM) -virtual machine. - -- [Live caption microphone demo.](#live-caption-microphone-demo) -- [Installation.](#installation) - - [Environment.](#environment) - - [Download the ONNX models.](#download-the-onnx-models) -- [Run the demo.](#run-the-demo) -- [Script notes.](#script-notes) - - [Speech truncation.](#speech-truncation) - - [Running on a slower processor.](#running-on-a-slower-processor) - - [Metrics.](#metrics) -- [Future work.](#future-work) -- [Citation.](#citation) - -# Installation. - -## Environment. - -Moonshine installation steps are available in the -[top level README](/README.md) of this repo. The same set of steps are included -here. - -First install the `uv` standalone installer as -[described here](https://github.com/astral-sh/uv?tab=readme-ov-file#installation). -Close the shell and re-open after the install. If you don't want to use `uv` -simply skip the virtual environment installation and subsequent activation, and -leave `uv` off the shell commands for `pip install`. - -Create the virtual environment and install dependences for Moonshine. -```console -cd -uv venv env_moonshine_demo -source env_moonshine_demo/bin/activate +# Demo: Live captioning from microphone input -uv pip install useful-moonshine@git+https://github.com/usefulsensors/moonshine.git -``` +This folder contains a demo of live captioning from microphone input, built on Moonshine. The script runs the Moonshine model on segments of speech detected in the microphone signal using a voice activity detector called [`silero-vad`](https://github.com/snakers4/silero-vad). The script prints scrolling text or "live captions" assembled from the model predictions to the console. -The demo requires these additional dependencies. -```console -uv pip install -r moonshine/moonshine/demo/requirements.txt -``` +The following steps have been tested in a `uv` (v0.4.25) virtual environment on these platforms: -If you can not find the `moonshine` repo folder in your home folder then -clone with git command and then run pip installs (this issue seen on an single -board computer running Ubuntu). -```console -cd -uv venv env_moonshine_demo -source env_moonshine_demo/bin/activate +- macOS 14.1 on a MacBook Pro M3 +- Ubuntu 22.04 VM on a MacBook Pro M2 +- Ubuntu 24.04 VM on a MacBook Pro M2 + +## Installation +### 0. Setup environment and install the Moonshine package + +Moonshine installation steps are available in the [top level README](/README.md) of this repo. Ensure you have the package installed before moving onto the next steps. + +### 1. Clone the repo and install extra dependencies + +You will need to clone the repo first: + +```shell git clone git@github.com:usefulsensors/moonshine.git +``` + +Then install the demo's extra requirements: -uv pip install useful-moonshine@git+https://github.com/usefulsensors/moonshine.git +```shell uv pip install -r moonshine/moonshine/demo/requirements.txt ``` -Ubuntu needs PortAudio installing for the package `sounddevice` to run. The -latest version 19.6.0-1.2build3 is suitable. -```console -cd +### Ubuntu: Install PortAudio + +Ubuntu needs PortAudio for the `sounddevice` package to run. The latest version (19.6.0-1.2build3 as of writing) is suitable. +```shell sudo apt update sudo apt upgrade -y sudo apt install -y portaudio19-dev ``` -## Download the ONNX models. +### 2. Prepare ONNX models -The script finds ONNX base or tiny models in the `demo/models/base` and -`demo/models/tiny` sub-folders. +The script finds ONNX base or tiny models in the `demo/models/base` and `demo/models/tiny` sub-folders. -TODO: add download instructions for the ONNX models. +TODO: add instructions for the ONNX models. -# Run the demo. +### 3. Run the demo -Check your microphone is connected and the microphone volume setting is not -muted in your host OS or system audio drivers. -```console -cd -source env_moonshine_demo/bin/activate +First, check that your microphone is connected and that the volume setting is not muted in your host OS or system audio drivers. +Then, run the demo: + +```shell python3 ./moonshine/moonshine/demo/live_captions.py base ``` -Speak in English language to the microphone and observe live captions in the -terminal. Quit the demo with ctrl + C to see console print of the captions. + +Speak in English language to the microphone and observe live captions in the terminal. Quit the demo with `Ctrl+C` to see a full printout of the captions. An example run on Ubuntu 24.04 VM on MacBook Pro M2 with Moonshine base ONNX model (Credit: BBC World Service). + ```console (env_moonshine_demo) parallels@ubuntu-linux-2404:~$ python3 moonshine/moonshine/demo/live_captions.py Error in cpuinfo: prctl(PR_SVE_GET_VL) failed @@ -112,56 +74,34 @@ Kamala Harris and Donald Trump have seen President Zelensky's victory plan setti (env_moonshine_demo) parallels@ubuntu-linux-2404:~$ ``` -# Script notes. +## Notes You may customize this script to display Moonshine text transcriptions as you wish. -The script `live_captions.py` loads the English language version of Moonshine -base ONNX model. The script includes logic to detect speech activity and limit -the context window of speech fed to the Moonshine model. The returned -transcriptions are displayed as scrolling captions. Speech segments with pauses -are cached and these cached captions are printed on exit. The printed captions -on exit will not contain the latest displayed caption when there was no pause -in the talker's speech prior to pressing ctrl + C. Stop speaking and wait -before pressing ctrl + C. If you are running on a slow or throttled processor -such that the model inferences are not realtime, after speaking stops you should -wait longer for the speech queue to be processed before pressing ctrl + C. - -## Speech truncation. - -Some hallucinations will be seen when the script is running: one reason is -speech gets truncated out of necessity to generate the frequent refresh and -timeout transcriptions. Truncated speech contains partial or sliced words for -which transcriber model transcriptions are unpredictable. See the printed -captions on script exit for the best results. - -## Running on a slower processor. -If you run this script on a slower processor consider using the `tiny` model. -```console -cd -source env_moonshine_demo/bin/activate +The script `live_captions.py` loads the English language version of Moonshine base ONNX model. It includes logic to detect speech activity and limit the context window of speech fed to the Moonshine model. The returned transcriptions are displayed as scrolling captions. Speech segments with pauses are cached and these cached captions are printed on exit. The printed captions on exit will not contain the latest displayed caption when there was no pause in the talker's speech prior to pressing `Ctrl+C`. Stop speaking and wait before pressing `Ctrl+C`. If you are running on a slow or throttled processor such that the model inferences are not realtime, after speaking stops you should wait longer for the speech queue to be processed before pressing `Ctrl+C`. + +### Speech truncation and hallucination + +Some hallucinations will be seen when the script is running: one reason is speech gets truncated out of necessity to generate the frequent refresh and timeout transcriptions. Truncated speech contains partial or sliced words for which transcriber model transcriptions are unpredictable. See the printed captions on script exit for the best results. +### Running on slower processors + +If you run this script on a slower processor, consider using the `tiny` model: + +```shell python3 ./moonshine/moonshine/demo/live_captions.py tiny ``` -The value of `MIN_REFRESH_SECS` will be ineffective when the model inference -time exceeds that value. Conversely on a faster processor consider reducing -the value of `MIN_REFRESH_SECS` for more frequent caption updates. On a slower -processor you might also consider reducing the value of `MAX_SPEECH_SECS` to -avoid slower model inferencing encountered with longer speech segments. +The value of `MIN_REFRESH_SECS` will be ineffective when the model inference time exceeds that value. Conversely on a faster processor consider reducing the value of `MIN_REFRESH_SECS` for more frequent caption updates. On a slower processor you might also consider reducing the value of `MAX_SPEECH_SECS` to avoid slower model inferencing encountered with longer speech segments. + +### Understanding metrics -## Metrics. -The metrics shown on program exit will vary based on the talker's speaking -style. If the talker speaks with more frequent pauses the speech segments are -shorter and the mean inference time will be lower. This is a feature of the -Moonshine model described in -[our paper](https://arxiv.org/abs/2410.15608). -When benchmarking use the same speech such as a recording of someone talking. +The metrics shown on program exit will vary based on the talker's speaking style. If the talker speaks with more frequent pauses, the speech segments are shorter and the mean inference time will be lower. This is a feature of the Moonshine model described in [our paper](https://arxiv.org/abs/2410.15608). When benchmarking, use the same speech, e.g., a recording of someone talking. -# Future work. +## TODO -* [x] ONNX runtime model version. +* [x] ONNX runtime model version -# Citation. +## Citation If you benefit from our work, please cite us: ``` diff --git a/moonshine/demo/example.mp4 b/moonshine/demo/example.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..e1aae2f7ce7564c15c824a3bfe11749517b9a7ae GIT binary patch literal 448017 zcmd42Wmp{B(l(4cB)F5og1fuB1b2tQ-7Pr53GVLh?(T%(8l2$4-QLOB&pv18`QE?Z z^`3RjtX^_gO|9;(yQ^!u!N9-(rp_Mr7EX3HU|pViI($03kII5Yhx_44M$Jv-hwDnmPlR85vpVm>HQ_K!fJa z&i3343~p|2^sW{rKs#$gTY5W3Glt*4LT~PDV-3RC**ja<**b9pj17$pjro`WjzCjB z7Jv!R$lA`>ijRq#k(&`0oVZf%z^FzCl@17ivwWqI8J= zqhkg*n>&IeK#G_F)^>JQhUTE2{@;Vl04Hk;V~{a_4=@639sl(s#uhe)&c6+@uyqDH zS{s5upm8H>7e_-6ePcTtdqZbX-x#!yoE;48=wfZY)VHy)1$6;V#z0%3v5PYwE8}l79Swi)Q%9haIY`{mSpVP4{XOq!%xCOq z3a~K(?bzST0(JP9S?C!74!;+}$4Ji(YTEx+{I9K{J0Ck2Xu!!CXwSzAu&@UmCD0iG 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b/moonshine/demo/README.md index f609fc1..b6c9d18 100644 --- a/moonshine/demo/README.md +++ b/moonshine/demo/README.md @@ -1,3 +1,6 @@ + +https://github.com/user-attachments/assets/aa65ef54-d4ac-4d31-864f-222b0e6ccbd3 + # Demo: Live captioning from microphone input This folder contains a demo of live captioning from microphone input, built on Moonshine. The script runs the Moonshine model on segments of speech detected in the microphone signal using a voice activity detector called [`silero-vad`](https://github.com/snakers4/silero-vad). The script prints scrolling text or "live captions" assembled from the model predictions to the console. @@ -70,7 +73,19 @@ ca at bbcworldservice.com/documentaries or wherever you get your BBC podcasts. model realtime factor : 21.20x Cached captions. -Kamala Harris and Donald Trump have seen President Zelensky's victory plan setting out his country's vision for the future. Ukrainians are just days away from discovering how much and for how long the next president of its biggest aid provider is willing to help them to stay in the fight. This edition of the inquiry was presented by me, Charmaine Cozier. The producer was Jill Collins, researcher Matt Dawson, editor Tara McDermott and Technica producer Ben Howton. This is the BBC World Service, and Alvin Hole is going home. This is Wakala County, Florida. When you cross that Kana line, oh yeah, you're going to feel free. As a child, I thought of it as a place of incredible beauty. But this is also a place where the past is very, very, Part from being the past. You go down the road and the like people left on the left And white people have on the right. I believe that to understand the United States, you need to know about places like Wakala County. It's a power struggle. That's what i feel you know fear Do you make you do a lot of things? In an election year, in a divided country, I've come home to see family and friends and to share their America. Alvin holes are the ramarica at bbcworldservice.com/documentaries or wherever you get your BBC podcasts. +Kamala Harris and Donald Trump have seen President Zelensky's victory plan setting out his country's +vision for the future. Ukrainians are just days away from discovering how much and for how long the +next president of its biggest aid provider is willing to help them to stay in the fight. This edition +of the inquiry was presented by me, Charmaine Cozier. The producer was Jill Collins, researcher Matt +Dawson, editor Tara McDermott and Technica producer Ben Howton. This is the BBC World Service, and Alvin +Hole is going home. This is Wakala County, Florida. When you cross that Kana line, oh yeah, you're going +to feel free. As a child, I thought of it as a place of incredible beauty. But this is also a place where +the past is very, very, Part from being the past. You go down the road and the like people left on the +left And white people have on the right. I believe that to understand the United States, you need to know +about places like Wakala County. It's a power struggle. That's what i feel you know fear Do you make you +do a lot of things? In an election year, in a divided country, I've come home to see family and friends +and to share their America. Alvin holes are the ramarica at bbcworldservice.com/documentaries or wherever +you get your BBC podcasts. 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b/moonshine/demo/README.md index 14d1f87..906950b 100644 --- a/moonshine/demo/README.md +++ b/moonshine/demo/README.md @@ -4,18 +4,18 @@ This directory contains various scripts to demonstrate the capabilities of the Moonshine ASR models. - [Moonshine Demos](#moonshine-demos) -- [Standalone file transcription.](#standalone-file-transcription) -- [Live caption microphone demo.](#live-caption-microphone-demo) +- [Demo: Standalone file transcription with ONNX](#demo-standalone-file-transcription-with-onnx) +- [Demo: Live captioning from microphone input](#demo-live-captioning-from-microphone-input) - [Installation.](#installation) - - [Environment.](#environment) - - [Download the ONNX models.](#download-the-onnx-models) - - [Run the demo.](#run-the-demo) - - [Script notes.](#script-notes) - - [Speech truncation.](#speech-truncation) - - [Running on a slower processor.](#running-on-a-slower-processor) - - [Metrics.](#metrics) -- [Future work.](#future-work) -- [Citation.](#citation) + - [0. Setup environment](#0-setup-environment) + - [1. Clone the repo and install extra dependencies](#1-clone-the-repo-and-install-extra-dependencies) + - [2. Download the ONNX models](#download-the-onnx-models) + - [Running the demo](#running-the-demo) + - [Script notes](#script-notes) + - [Speech truncation and hallucination](#speech-truncation-and-hallucination) + - [Running on a slower processor](#running-on-a-slower-processor) + - [Metrics](#metrics) +- [Citation](#citation) # Demo: Standalone file transcription with ONNX @@ -26,26 +26,26 @@ package alone, without depending on `torch` or `tensorflow`. This enables running on SBCs such as Raspberry Pi. Follow the instructions below to setup and run. -* Install `onnxruntime` (or `onnxruntime-gpu` if you want to run on GPUs) and `tokenizers` packages using your Python package manager of choice, such as `pip`. +1. Install `onnxruntime` (or `onnxruntime-gpu` if you want to run on GPUs) and `tokenizers` packages using your Python package manager of choice, such as `pip`. -* Download the `onnx` files from huggingface hub to a directory. +2. Download the `onnx` files from huggingface hub to a directory. - ```shell - mkdir moonshine_base_onnx - cd moonshine_base_onnx - wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/preprocess.onnx - wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/encode.onnx - wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/uncached_decode.onnx - wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/cached_decode.onnx - cd .. - ``` +```shell +mkdir moonshine_base_onnx +cd moonshine_base_onnx +wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/preprocess.onnx +wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/encode.onnx +wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/uncached_decode.onnx +wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/cached_decode.onnx +cd .. +``` -* Run `onnx_standalone.py` to transcribe a wav file +3. Run `onnx_standalone.py` to transcribe a wav file - ```shell - moonshine/moonshine/demo/onnx_standalone.py --models_dir moonshine_base_onnx --wav_file moonshine/moonshine/assets/beckett.wav - ['Ever tried ever failed, no matter try again fail again fail better.'] - ``` +```shell +moonshine/moonshine/demo/onnx_standalone.py --models_dir moonshine_base_onnx --wav_file moonshine/moonshine/assets/beckett.wav +['Ever tried ever failed, no matter try again fail again fail better.'] +``` # Demo: Live captioning from microphone input @@ -62,9 +62,9 @@ The following steps have been tested in a `uv` (v0.4.25) virtual environment on ## Installation -### 0. Setup environment and install the Moonshine package +### 0. Setup environment -Moonshine installation steps are available in the [top level README](/README.md) of this repo. Ensure you have the package installed before moving onto the next steps. +Steps to set up a virtual environment are available in the [top level README](/README.md) of this repo. Note that this demo is standalone and has no requirement to install the `useful-moonshine` package. Instead, you will clone the repo. ### 1. Clone the repo and install extra dependencies @@ -80,9 +80,10 @@ Then install the demo's extra requirements: uv pip install -r moonshine/moonshine/demo/requirements.txt ``` -### Ubuntu: Install PortAudio +#### Ubuntu: Install PortAudio Ubuntu needs PortAudio for the `sounddevice` package to run. The latest version (19.6.0-1.2build3 as of writing) is suitable. + ```shell sudo apt update sudo apt upgrade -y @@ -96,7 +97,7 @@ The script finds ONNX base or tiny models in the sub-folders. Download Moonshine `onnx` model files from HuggingFace hub. -```console +```shell cd mkdir moonshine/moonshine/demo/models mkdir moonshine/moonshine/demo/models/moonshine_base_onnx @@ -117,18 +118,21 @@ wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/tiny/uncac wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/tiny/cached_decode.onnx ``` -### 3. Run the demo +## Running the demo -First, check that your microphone is connected and that the volume setting is not muted in your host OS or system audio drivers. +First, check that your microphone is connected and that the volume setting is not muted in your host OS or system audio drivers. Then, run the script: ``` shell python3 moonshine/moonshine/demo/live_captions.py ``` -Speak in English language to the microphone and observe live captions in the terminal. Quit the demo with `Ctrl+C` to see a full printout of the captions. +By default, this will run the demo with the Moonshine base ONNX model. Supported arguments are `moonshine_base_onnx` and `moonshine_tiny_onnx`. + +When running, speak in English language to the microphone and observe live captions in the terminal. Quit the demo with `Ctrl+C` to see a full printout of the captions. An example run on Ubuntu 24.04 VM on MacBook Pro M2 with Moonshine base ONNX -model. +model: + ```console (env_moonshine_demo) parallels@ubuntu-linux-2404:~$ python3 moonshine/moonshine/demo/live_captions.py Error in cpuinfo: prctl(PR_SVE_GET_VL) failed @@ -149,7 +153,7 @@ This is an example of the Moonshine base model being used to generate live capti (env_moonshine_demo) parallels@ubuntu-linux-2404:~$ ``` -For comparison this is the Faster-Whisper int8 base model on the same instance. +For comparison, this is the `faster-whisper` int8 base model on the same instance. The value of `MIN_REFRESH_SECS` was increased as the model inference is too slow for a value of 0.2 seconds. @@ -193,7 +197,7 @@ python3 ./moonshine/moonshine/demo/live_captions.py moonshine_tiny_onnx The value of `MIN_REFRESH_SECS` will be ineffective when the model inference time exceeds that value. Conversely on a faster processor consider reducing the value of `MIN_REFRESH_SECS` for more frequent caption updates. On a slower processor you might also consider reducing the value of `MAX_SPEECH_SECS` to avoid slower model inferencing encountered with longer speech segments. -### Understanding metrics +### Metrics The metrics shown on program exit will vary based on the talker's speaking style. If the talker speaks with more frequent pauses, the speech segments are shorter and the mean inference time will be lower. This is a feature of the Moonshine model described in [our paper](https://arxiv.org/abs/2410.15608). When benchmarking, use the same speech, e.g., a recording of someone talking. From 682da359d9ea52015a93ecc8dc19b8429a432130 Mon Sep 17 00:00:00 2001 From: Evan King Date: Fri, 25 Oct 2024 11:30:04 -0500 Subject: [PATCH 5/7] Adjust formatting --- moonshine/demo/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/moonshine/demo/README.md b/moonshine/demo/README.md index 906950b..10f7669 100644 --- a/moonshine/demo/README.md +++ b/moonshine/demo/README.md @@ -20,7 +20,7 @@ Moonshine ASR models. # Demo: Standalone file transcription with ONNX -The script [onnx_standalone.py](/moonshine/demo/onnx_standalone.py) +The script [`onnx_standalone.py`](/moonshine/demo/onnx_standalone.py) demonstrates how to run a Moonshine model with the `onnxruntime` package alone, without depending on `torch` or `tensorflow`. This enables running on SBCs such as Raspberry Pi. Follow the instructions below to setup @@ -189,7 +189,7 @@ Some hallucinations will be seen when the script is running: one reason is speec ### Running on a slower processor -If you run this script on a slower processor consider using the `tiny` model. +If you run this script on a slower processor, consider using the `tiny` model. ```shell python3 ./moonshine/moonshine/demo/live_captions.py moonshine_tiny_onnx From 6c1e62fa9d1612b37144e6ccd5beee378260e527 Mon Sep 17 00:00:00 2001 From: Evan King Date: Fri, 25 Oct 2024 11:48:43 -0500 Subject: [PATCH 6/7] Switch to argparse for arg parsing --- moonshine/demo/README.md | 4 ++-- moonshine/demo/live_captions.py | 15 +++++++++++---- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/moonshine/demo/README.md b/moonshine/demo/README.md index 10f7669..b2c45fe 100644 --- a/moonshine/demo/README.md +++ b/moonshine/demo/README.md @@ -126,7 +126,7 @@ First, check that your microphone is connected and that the volume setting is no python3 moonshine/moonshine/demo/live_captions.py ``` -By default, this will run the demo with the Moonshine base ONNX model. Supported arguments are `moonshine_base_onnx` and `moonshine_tiny_onnx`. +By default, this will run the demo with the Moonshine base ONNX model. The `--model_size` argument sets the model to use: supported arguments are `moonshine_base_onnx` and `moonshine_tiny_onnx`. When running, speak in English language to the microphone and observe live captions in the terminal. Quit the demo with `Ctrl+C` to see a full printout of the captions. @@ -192,7 +192,7 @@ Some hallucinations will be seen when the script is running: one reason is speec If you run this script on a slower processor, consider using the `tiny` model. ```shell -python3 ./moonshine/moonshine/demo/live_captions.py moonshine_tiny_onnx +python3 ./moonshine/moonshine/demo/live_captions.py --model_size moonshine_tiny_onnx ``` The value of `MIN_REFRESH_SECS` will be ineffective when the model inference time exceeds that value. Conversely on a faster processor consider reducing the value of `MIN_REFRESH_SECS` for more frequent caption updates. On a slower processor you might also consider reducing the value of `MAX_SPEECH_SECS` to avoid slower model inferencing encountered with longer speech segments. diff --git a/moonshine/demo/live_captions.py b/moonshine/demo/live_captions.py index 2d1d556..6735ff4 100644 --- a/moonshine/demo/live_captions.py +++ b/moonshine/demo/live_captions.py @@ -2,6 +2,7 @@ import os import sys import time +import argparse from queue import Queue @@ -100,10 +101,16 @@ def print_captions(text, new_cached_caption=False): if __name__ == '__main__': - model_size = "moonshine_base_onnx" if len(sys.argv) < 2 else sys.argv[1] - if model_size not in ["moonshine_base_onnx", "moonshine_tiny_onnx"]: - raise ValueError(f"Model size {model_size} is not supported.") - + parser = argparse.ArgumentParser( + prog="live_captions", + description="Live captioning demo of Moonshine models", + ) + parser.add_argument( + "--model_size", help="Model to run the demo with", default="moonshine_base_onnx", + choices=["moonshine_base_onnx", "moonshine_tiny_onnx"] + ) + args = parser.parse_args() + model_size = args.model_size models_dir = os.path.join(os.path.dirname(__file__), 'models', f"{model_size}") print(f"Loading Moonshine model '{models_dir}' ...") transcribe = Transcriber(models_dir=models_dir, rate=SAMPLING_RATE) From ff4f206ddf9a80230d7e3f43496b7d97c74f2aa8 Mon Sep 17 00:00:00 2001 From: evmaki Date: Fri, 25 Oct 2024 17:09:45 +0000 Subject: [PATCH 7/7] Apply code formatting --- moonshine/demo/live_captions.py | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/moonshine/demo/live_captions.py b/moonshine/demo/live_captions.py index 49afa7a..bf9e14f 100644 --- a/moonshine/demo/live_captions.py +++ b/moonshine/demo/live_captions.py @@ -1,4 +1,5 @@ """Live captions from microphone using Moonshine and SileroVAD ONNX models.""" + import os import sys import time @@ -62,10 +63,12 @@ def __call__(self, speech): def create_input_callback(q): """Callback method for sounddevice InputStream.""" + def input_callback(data, frames, time, status): if status: print(status) q.put((data.copy(), status)) + return input_callback @@ -81,33 +84,34 @@ def end_recording(speech, marker=""): def print_captions(text, new_cached_caption=False): """Prints right justified on same line, prepending cached captions.""" - print('\r' + " " * MAX_LINE_LENGTH, end='', flush=True) + print("\r" + " " * MAX_LINE_LENGTH, end="", flush=True) if len(text) > MAX_LINE_LENGTH: text = text[-MAX_LINE_LENGTH:] elif text != "\n": for caption in caption_cache[::-1]: - text = (caption[:-MARKER_LENGTH] if not VERBOSE else - caption + " ") + text + text = (caption[:-MARKER_LENGTH] if not VERBOSE else caption + " ") + text if len(text) > MAX_LINE_LENGTH: break if len(text) > MAX_LINE_LENGTH: text = text[-MAX_LINE_LENGTH:] text = " " * (MAX_LINE_LENGTH - len(text)) + text - print('\r' + text, end='', flush=True) + print("\r" + text, end="", flush=True) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser( prog="live_captions", description="Live captioning demo of Moonshine models", ) parser.add_argument( - "--model_size", help="Model to run the demo with", default="moonshine_base_onnx", - choices=["moonshine_base_onnx", "moonshine_tiny_onnx"] + "--model_size", + help="Model to run the demo with", + default="moonshine_base_onnx", + choices=["moonshine_base_onnx", "moonshine_tiny_onnx"], ) args = parser.parse_args() model_size = args.model_size - models_dir = os.path.join(os.path.dirname(__file__), 'models', f"{model_size}") + models_dir = os.path.join(os.path.dirname(__file__), "models", f"{model_size}") print(f"Loading Moonshine model '{models_dir}' ...") transcribe = Transcriber(models_dir=models_dir, rate=SAMPLING_RATE) @@ -153,11 +157,11 @@ def print_captions(text, new_cached_caption=False): speech_dict = vad_iterator(chunk) if speech_dict: - if 'start' in speech_dict and not recording: + if "start" in speech_dict and not recording: recording = True start_time = time.time() - if 'end' in speech_dict and recording: + if "end" in speech_dict and recording: recording = False end_recording(speech, "")

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