timBoML
/

2cent-tts-60m

@@ -3,197 +3,71 @@ library_name: transformers
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 tags: []
 ---
+Converted the models from https://github.com/taylorchu/2cent-tts to .safetensors. Below is inference code:
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "timBoML/2cent-tts-60m"
+)
+tokenizer = AutoTokenizer.from_pretrained("timBoML/2cent-tts-60m")
+phones = "həlˈoʊ aɪɐm tˈuː sˈɛnt tˌiːtˌiːˈɛs" # using espeak-ng
+input_ids = (
+    tokenizer.encode(phones, add_special_tokens=False)
+    + tokenizer.encode("<s>", add_special_tokens=False)
+    + [4136]
+)
+input_ids = torch.tensor(input_ids).unsqueeze(0)
+generated_ids = model.generate(
+      input_ids=input_ids,
+      max_new_tokens=2048,
+  )
+generated_ids = generated_ids.squeeze()
+tokens = generated_ids[input_ids.shape[1]:]
+first_audio_token = tokenizer.encode("<audio_0>")[-1]
+tokens = tokens - first_audio_token
+import locale
+import torchaudio.transforms as T
+import os
+import torch
+from snac import SNAC
+locale.getpreferredencoding = lambda: "UTF-8"
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+def redistribute_codes(code_list):
+  layer_1 = []
+  layer_2 = []
+  layer_3 = []
+  for i in range((len(code_list)+1)//7):
+    layer_1.append(code_list[7*i])
+    layer_2.append(code_list[7*i+1])
+    layer_3.append(code_list[7*i+2])
+    layer_3.append(code_list[7*i+3])
+    layer_2.append(code_list[7*i+4])
+    layer_3.append(code_list[7*i+5])
+    layer_3.append(code_list[7*i+6])
+  codes = [torch.tensor(layer_1).unsqueeze(0),
+         torch.tensor(layer_2).unsqueeze(0),
+         torch.tensor(layer_3).unsqueeze(0)]
+  audio_hat = snac_model.decode(codes)
+  return audio_hat
+sample = redistribute_codes(tokens)
+from IPython.display import Audio, display
+display(Audio(sample.detach().squeeze().to("cpu").numpy(), rate=24000))
+```