Update the order of code and add pipeline usage! (#22)

- Update the order of code and add pipeline usage! (578a8302e3794675594bb02d971245bb7cf5690e)


Co-authored-by: Vaibhav Srivastav <[email protected]>

README.md

@@ -47,44 +47,35 @@ Extensive evaluations show the superiority of the proposed SpeechT5 framework on
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-You can use this model for speech synthesis. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you.
-
-## Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+## How to Get Started With the Model
 
-# Bias, Risks, and Limitations
+You can access the SpeechT5 model via the `Text-to-Speech` pipeline in just a couple lines of code!
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```python
+# Following pip packages need to be installed:
+# !pip install transformers sentencepiece datasets
 
-[More Information Needed]
+from transformers import pipeline
+from datasets import load_dataset
+import soundfile as sf
 
-## Recommendations
+synthesiser = pipeline("text-to-speech", "microsoft/speech_tt5")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+# You can replace this embedding with your own as well.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+speech = pipe("Hello what is happening", forward_params={"speaker_embeddings": speaker_embeddings})
 
+sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
 
-## How to Get Started With the Model
+```
 
-Use the code below to convert text into a mono 16 kHz speech waveform.
+For more fine-grained control you can use the processor + generate code to convert text into a mono 16 kHz speech waveform.
 
 ```python
 # Following pip packages need to be installed:
-# !pip install git+https://github.com/huggingface/transformers sentencepiece datasets
+# !pip install transformers sentencepiece datasets
 
 from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
 from datasets import load_dataset
@@ -111,6 +102,37 @@ sf.write("speech.wav", speech.numpy(), samplerate=16000)
 
 Refer to [this Colab notebook](https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ) for an example of how to fine-tune SpeechT5 for TTS on a different dataset or a new language.
 
+
+## Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+You can use this model for speech synthesis. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you.
+
+## Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+## Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+## Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
 # Training Details
 
 ## Training Data