Merge pull request #7 from bghira/documentation/mps-xpu-example

add mps and xpu to examples

README.md

@@ -25,6 +25,12 @@ Parler-TTS has light-weight dependencies and can be installed in one line:
 pip install git+https://github.com/huggingface/parler-tts.git
 ```
 
+Apple Silicon users will need to run a follow-up command to make use the nightly PyTorch (2.4) build for bfloat16 support:
+
+```sh
+pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
+```
+
 ## Usage
 
 > [!TIP]
@@ -38,9 +44,16 @@ from transformers import AutoTokenizer
 import soundfile as sf
 import torch
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda:0"
+if torch.backends.mps.is_available():
+    device = "mps"
+if torch.xpu.is_available():
+    device = "xpu"
+torch_dtype = torch.float16 if device != "cpu" else torch.float32
 
-model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_mini_v0.1").to(device)
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_mini_v0.1").to(device, dtype=torch_dtype)
 tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")
 
 prompt = "Hey, how are you doing today?"
@@ -49,7 +62,7 @@ description = "A female speaker with a slightly low-pitched voice delivers her w
 input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
 prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
 
-generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids).to(torch.float32)
 audio_arr = generation.cpu().numpy().squeeze()
 sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
 ```