Merge pull request #84 from sanchit-gandhi/u7-all-together

U7: putting it all together

chapters/en/_toctree.yml

@@ -103,27 +103,23 @@
  - local: chapter6/supplemental_reading
  title: Supplemental reading and resources
 
-#- title: Unit 7. Audio to audio
-# sections:
-# - local: chapter7/introduction
-# title: What you'll learn and what you'll build
-# - local: chapter7/tasks
-# title: Examples of audio-to-audio tasks
-# - local: chapter7/choosing_dataset
-# title: Choosing a dataset
-# - local: chapter7/preprocessing
-# title: Loading and preprocessing data
-# - local: chapter7/evaluation
-# title: Evaluation metrics for audio-to-audio
-# - local: chapter7/fine-tuning
-# title: Fine-tuning the model
+- title: Unit 7. Putting it all together
+ sections:
+ - local: chapter7/introduction
+ title: What you'll learn and what you'll build
+ - local: chapter7/speech-to-speech
+ title: Speech-to-speech translation
+ - local: chapter7/voice-assistant
+ title: Creating a voice assistant
+ - local: chapter7/transcribe-meeting
+ title: Transcribe a meeting
 # - local: chapter7/quiz
 # title: Quiz
 # quiz: 7
-# - local: chapter7/hands_on
-# title: Hands-on exercise
-# - local: chapter7/supplemental_reading
-# title: Supplemental reading and resources
+ - local: chapter7/hands-on
+ title: Hands-on exercise
+ - local: chapter7/supplemental_reading
+ title: Supplemental reading and resources
 #
 #- title: Unit 8. Finish line
 # sections:

chapters/en/chapter7/hands-on.mdx

@@ -0,0 +1,47 @@
+# Hands-on exercise
+
+In this Unit, we consolidated the material covered in the previous six units of the course to build three integrated
+audio applications. As you've experienced, building more involved audio tools is fully within reach by using the 
+foundational skills you've acquired in this course.
+
+The hands-on exercise takes one of the applications covered in this Unit, and extends it with a few multilingual 
+tweaks 🌍 Your objective is to take the [cascaded speech-to-speech translation Gradio demo](https://huggingface.co/spaces/course-demos/speech-to-speech-translation)
+from the first section in this Unit, and update it to translate to any **non-English** language. That is to say, the 
+demo should take speech in language X, and translate it to speech in language Y, where the target language Y is not
+English. You should start by [duplicating](https://huggingface.co/spaces/course-demos/speech-to-speech-translation?duplicate=true)
+the template under your Hugging Face namespace. There's no requirement to use a GPU accelerator device - the free CPU
+tier works just fine 🤗 However, you should ensure that the visibility of your demo is set to **public**. This is required
+such that your demo is accessible to us and can thus be checked for correctness.
+
+Tips for updating the speech translation function to perform multilingual speech translation are provided in the 
+section on [speech-to-speech translation](speech-to-speech.mdx). By following these instructions, you should be able
+to update the demo to translate from speech in language X to text in language Y, which is half of the task!
+
+To synthesise from text in language Y to speech in language Y, where Y is a multilingual language, you will need 
+to use a multilingual TTS checkpoint. For this, you can either use the SpeechT5 TTS checkpoint that you fine-tuned 
+in the previous hands-on exercise, or a pre-trained multilingual TTS checkpoint. There are two options for pre-trained 
+checkpoints, either the checkpoint [sanchit-gandhi/speecht5_tts_vox_nl](https://huggingface.co/sanchit-gandhi/speecht5_tts_vox_nl),
+which is a SpeechT5 checkpoint fine-tuned on the Dutch split of the [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli)
+dataset, or an MMS TTS checkpoint (see section on [pretrained models for TTS](../chapter6/pre-trained_models.mdx)).
+
+<Tip>
+ In our experience experimenting with the Dutch language, using an MMS TTS checkpoint results in better performance than a
+ fine-tuned SpeechT5 one, but you might find that your fine-tuned TTS checkpoint is preferable in your language.
+ If you decide to use an MMS TTS checkpoint, you will need to update the <a href="https://huggingface.co/spaces/course-demos/speech-to-speech-translation/blob/a03175878f522df7445290d5508bfb5c5178f787/requirements.txt#L2">requirements.txt</a>
+ file of your demo to install <code>transformers</code> from the PR branch:
+ <p><code>git+https://github.com/hollance/transformers.git@6900e8ba6532162a8613d2270ec2286c3f58f57b</code></p>
+</Tip>
+
+
+Your demo should take as input an audio file, and return as output another audio file, matching the signature of the 
+[`speech_to_speech_translation`](https://huggingface.co/spaces/course-demos/speech-to-speech-translation/blob/3946ba6705a6632a63de8672ac52a482ab74b3fc/app.py#L35)
+function in the template demo. Therefore, we recommend that you leave the main function `speech_to_speech_translation` 
+as is, and only update the [`translate`](https://huggingface.co/spaces/course-demos/speech-to-speech-translation/blob/a03175878f522df7445290d5508bfb5c5178f787/app.py#L24)
+and [`synthesise`](https://huggingface.co/spaces/course-demos/speech-to-speech-translation/blob/a03175878f522df7445290d5508bfb5c5178f787/app.py#L29)
+functions as required.
+
+Once you have built your demo as a Gradio demo on the Hugging Face Hub, you can submit it for assessment. Head to the 
+Space [audio-course-u7-assessment](https://huggingface.co/spaces/huggingface-course/audio-course-u7-assessment) and 
+provide the repository id of your demo when prompted. This Space will check that your demo has been built correctly by 
+sending a sample audio file to your demo and checking that the returned audio file is indeed non-English. If your demo 
+works correctly, you'll get a green tick next to your name on the overall [progress space](https://huggingface.co/spaces/MariaK/Check-my-progress-Audio-Course) ✅

chapters/en/chapter7/introduction.mdx

@@ -0,0 +1,16 @@
+# Unit 7. Putting it all together 🪢
+
+Well done on making it to Unit 7 🥳 You're just a few steps away from completing the course and acquiring the final few
+skills you need to navigate the field of Audio ML. In terms of understanding, you already know everything there is to know!
+Together, we've comprehensively covered the main topics that constitute the audio domain and their accompanying theory 
+(audio data, audio classification, speech recognition and text-to-speech). What this Unit aims to deliver is a framework 
+for **putting it all together**: now that you know how each of these tasks work in isolation, we're going to explore how
+you can combine them together to build some real-world applications.
+
+## What you'll learn and what you'll build
+
+In this Unit, we'll cover the following three topics:
+
+* [Speech-to-speech translation](speech-to-speech): translate speech from one language into speech in a different language
+* [Creating a voice assistant](voice-assistant): build your own voice assistant that works in a similar way to Alexa or Siri
+* [Transcribing meetings](transcribe-meeting): transcribe a meeting and label the transcript with who spoke when