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audio/tacotron2_pipeline_tutorial.py at main · pytorch ...
https://github.com/pytorch/audio/blob/main/examples/tutorials/tacotron2_pipeline_tutorial.py
# This tutorial shows how to build text-to-speech pipeline, using the # pretrained Tacotron2 in torchaudio. # # The text-to-speech pipeline goes as follows: # # 1. Text preprocessing # # First, the input text is encoded into a list of symbols. In this # tutorial, we will use English characters and phonemes as the symbols. # # 2. Spectrogram generation #
Audio Pipeline — Audio Development Framework documentation
https://docs.espressif.com/projects/esp-adf/en/latest/api-reference/framework/audio_pipeline.html
esp_err_t audio_pipeline_link_insert (audio_pipeline_handle_t pipeline, bool first, audio_element_handle_t prev, ringbuf_handle_t conect_rb, audio_element_handle_t next) ¶ Insert the specific audio_element to audio_pipeline, previous element connect to the next element by ring buffer. Return. ESP_OK. ESP_FAIL . Parameters [in] pipeline: The audio pipeline handle
Speech Recognition with Wav2Vec2 — Torchaudio 0.10.0 ...
https://pytorch.org/audio/stable/tutorials/speech_recognition_pipeline_tutorial.html
In this tutorial, we looked at how to use torchaudio.pipelines to perform acoustic feature extraction and speech recognition. Constructing a model and getting the emission is as short as two lines. model = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H.get_model() emission = model(waveforms, ...)
Text-to-Speech with Tacotron2 — Torchaudio 0.10.0 ...
https://pytorch.org/audio/stable/tutorials/tacotron2_pipeline_tutorial.html
This tutorial shows how to build text-to-speech pipeline, using the pretrained Tacotron2 in torchaudio. The text-to-speech pipeline goes as follows: Text preprocessing. First, the input text is encoded into a list of symbols. In this tutorial, we will use English characters and phonemes as the symbols. Spectrogram generation
Speech Recognition with Wav2Vec2 — Torchaudio main ...
https://pytorch.org/audio/master/tutorials/speech_recognition_pipeline_tutorial.html
Creating a pipeline. First, we will create a Wav2Vec2 model that performs the feature extraction and the classification. There are two types of Wav2Vec2 pre-trained weights available in torchaudio. The ones fine-tuned for ASR task, and the ones not fine-tuned. Wav2Vec2 (and HuBERT) models are trained in self-supervised manner.
Speech Recognition with Wav2Vec2 — Torchaudio nightly ...
https://pytorch.org/audio/main/tutorials/speech_recognition_pipeline_tutorial.html
Wav2Vec2 (and HuBERT) models are trained in self-supervised manner. They are firstly trained with audio only for representation learning, then fine-tuned for a specific task with additional labels. The pre-trained weights without fine-tuning can be fine-tuned for other downstream tasks as well, but this tutorial does not cover that.
SOLAR PIPELINE TUTORIAL: 8-MIN DEMO - YouTube
https://www.youtube.com/watch?v=usL_crG0DYU
Click the link to get your 14-day FREE trial of Solar PIPELINE: https://solarpipeline.com/subscriptio...Solar Pipeline was created by solar pros for solar pr...
pipelineaudio.net – Your Source for EVERYTHING in Audio ...
https://pipelineaudio.net/
Your source for EVERYTHING in audio engineering. If you are doing anything related to audio production or music, we would love to help! From developing the tools we use to record, all the way to remote recording, its audio engineering all the way! Learn More. Testimonials.
Basic tutorial 3: Dynamic pipelines
https://gstreamer.freedesktop.org/documentation/tutorials/basic/dynamic-pipelines.html
This tutorial shows the rest of the basic concepts required to use GStreamer, which allow building the pipeline "on the fly", as information becomes available, instead of having a monolithic pipeline defined at the beginning of your application. After this tutorial, you will have the necessary knowledge to start the Playback tutorials. The points reviewed here will be:
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