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orpheus-swedish

Text Generation
Transformers
Safetensors
llama
conversational
text-generation-inference
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 Community
Dan commited on
Commit
e4f521b
·
1 Parent(s): 136c393

add pipeline.py correct

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Files changed (3) hide show
  1. pipeline/pipeline.py +110 -3
  2. pyproject.toml +1 -0
  3. uv.lock +100 -0
pipeline/pipeline.py CHANGED
@@ -2,6 +2,7 @@ from transformers import Pipeline
2
  from snac import SNAC
3
  import torch
4
  from transformers import AutoModelForCausalLM, AutoTokenizer
 
5
 
6
 
7
  class MyPipeline(Pipeline):
@@ -15,16 +16,42 @@ class MyPipeline(Pipeline):
15
  tokeniser_name = "meta-llama/Llama-3.2-3B-Instruct"
16
  model_name = "cubbk/orpheus-swedish"
17
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18
  self.model = AutoModelForCausalLM.from_pretrained(
19
  model_name, torch_dtype=torch.bfloat16
20
  )
21
  self.model.cuda()
22
  self.tokenizer = AutoTokenizer.from_pretrained(model_name)
23
 
 
 
24
  def _sanitize_parameters(self, **kwargs):
25
  return {}, {}, {}
26
 
27
  def preprocess(self, inputs, args=2):
 
 
28
  all_input_ids = []
29
  for prompt in inputs:
30
  input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
@@ -97,11 +124,91 @@ class MyPipeline(Pipeline):
97
  return generated_ids
98
 
99
  def postprocess(self, model_outputs):
100
- return model_outputs
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
101
 
102
 
103
  if __name__ == "__main__":
104
  pipe = MyPipeline()
105
- prompt = "Hej, hur mår du?"
 
 
 
 
 
 
 
 
 
106
  outputs = pipe(prompt)
107
- print(outputs)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2
  from snac import SNAC
3
  import torch
4
  from transformers import AutoModelForCausalLM, AutoTokenizer
5
+ from huggingface_hub import snapshot_download
6
 
7
 
8
  class MyPipeline(Pipeline):
 
16
  tokeniser_name = "meta-llama/Llama-3.2-3B-Instruct"
17
  model_name = "cubbk/orpheus-swedish"
18
 
19
+ # Download only model config and safetensors
20
+ model_path = snapshot_download(
21
+ repo_id=model_name,
22
+ allow_patterns=[
23
+ "config.json",
24
+ "*.safetensors",
25
+ "model.safetensors.index.json",
26
+ ],
27
+ ignore_patterns=[
28
+ "optimizer.pt",
29
+ "pytorch_model.bin",
30
+ "training_args.bin",
31
+ "scheduler.pt",
32
+ "tokenizer.json",
33
+ "tokenizer_config.json",
34
+ "special_tokens_map.json",
35
+ "vocab.json",
36
+ "merges.txt",
37
+ "tokenizer.*",
38
+ ],
39
+ )
40
+
41
  self.model = AutoModelForCausalLM.from_pretrained(
42
  model_name, torch_dtype=torch.bfloat16
43
  )
44
  self.model.cuda()
45
  self.tokenizer = AutoTokenizer.from_pretrained(model_name)
46
 
47
+ super().__init__(model=self.model, tokenizer=self.tokenizer)
48
+
49
  def _sanitize_parameters(self, **kwargs):
50
  return {}, {}, {}
51
 
52
  def preprocess(self, inputs, args=2):
53
+ inputs = [inputs]
54
+
55
  all_input_ids = []
56
  for prompt in inputs:
57
  input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
 
124
  return generated_ids
125
 
126
  def postprocess(self, model_outputs):
127
+ # @title Parse Output as speech
128
+
129
+ generated_ids = model_outputs
130
+
131
+ token_to_find = 128257
132
+ token_to_remove = 128258
133
+
134
+ token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
135
+
136
+ if len(token_indices[1]) > 0:
137
+ last_occurrence_idx = token_indices[1][-1].item()
138
+ cropped_tensor = generated_ids[:, last_occurrence_idx + 1 :]
139
+ else:
140
+ cropped_tensor = generated_ids
141
+
142
+ mask = cropped_tensor != token_to_remove
143
+
144
+ processed_rows = []
145
+
146
+ for row in cropped_tensor:
147
+ masked_row = row[row != token_to_remove]
148
+ processed_rows.append(masked_row)
149
+
150
+ code_lists = []
151
+
152
+ for row in processed_rows:
153
+ row_length = row.size(0)
154
+ new_length = (row_length // 7) * 7
155
+ trimmed_row = row[:new_length]
156
+ trimmed_row = [t - 128266 for t in trimmed_row]
157
+ code_lists.append(trimmed_row)
158
+
159
+ def redistribute_codes(code_list):
160
+ layer_1 = []
161
+ layer_2 = []
162
+ layer_3 = []
163
+ for i in range((len(code_list) + 1) // 7):
164
+ layer_1.append(code_list[7 * i])
165
+ layer_2.append(code_list[7 * i + 1] - 4096)
166
+ layer_3.append(code_list[7 * i + 2] - (2 * 4096))
167
+ layer_3.append(code_list[7 * i + 3] - (3 * 4096))
168
+ layer_2.append(code_list[7 * i + 4] - (4 * 4096))
169
+ layer_3.append(code_list[7 * i + 5] - (5 * 4096))
170
+ layer_3.append(code_list[7 * i + 6] - (6 * 4096))
171
+ codes = [
172
+ torch.tensor(layer_1).unsqueeze(0),
173
+ torch.tensor(layer_2).unsqueeze(0),
174
+ torch.tensor(layer_3).unsqueeze(0),
175
+ ]
176
+ audio_hat = self.snac_model.decode(codes)
177
+ return audio_hat
178
+
179
+ my_samples = []
180
+ for code_list in code_lists:
181
+ samples = redistribute_codes(code_list)
182
+ my_samples.append(samples)
183
+ return my_samples
184
 
185
 
186
  if __name__ == "__main__":
187
  pipe = MyPipeline()
188
+
189
+ prompt = [
190
+ "Billströms kritik är extra känslig för statsministern i och med att Billström tillhört den innersta kretsen i regeringen och partiet. ",
191
+ "Möjligen har också Billström känt sig trampad på tårna och kritiken mot NSR kan ha varit en bidragande orsak till att han förra året oväntat avgick som utrikesminister."
192
+ "Tack",
193
+ "Hej, hur mår du?",
194
+ "Vad är klockan?",
195
+ "Vilken färg har himlen?",
196
+ "Vad är meningen med livet?",
197
+ ]
198
  outputs = pipe(prompt)
199
+
200
+ # if a notebook:
201
+
202
+ # from IPython.display import display, Audio
203
+ # for i in range(len(outputs)):
204
+ # print(prompt[i])
205
+ # samples = outputs[i][0]
206
+ # display(Audio(samples.detach().squeeze().to("cpu").numpy(), rate=24000))
207
+
208
+ # else save to wav files
209
+ import soundfile as sf
210
+
211
+ for i in range(len(outputs)):
212
+ print(prompt[i])
213
+ samples = outputs[i][0]
214
+ sf.write(f"audio_{i}.wav", samples.detach().squeeze().to("cpu").numpy(), 24000)
pyproject.toml CHANGED
@@ -7,6 +7,7 @@ requires-python = ">=3.11"
7
  dependencies = [
8
  "numpy>=2.3.3",
9
  "snac>=1.2.1",
 
10
  "torch>=2.8.0",
11
  "transformers>=4.56.1",
12
  ]
 
7
  dependencies = [
8
  "numpy>=2.3.3",
9
  "snac>=1.2.1",
10
+ "soundfile>=0.13.1",
11
  "torch>=2.8.0",
12
  "transformers>=4.56.1",
13
  ]
uv.lock CHANGED
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18
  [[package]]
19
  name = "charset-normalizer"
20
  version = "3.4.3"
@@ -439,6 +509,7 @@ source = { virtual = "." }
439
  dependencies = [
440
  { name = "numpy" },
441
  { name = "snac" },
 
442
  { name = "torch" },
443
  { name = "transformers" },
444
  ]
@@ -447,6 +518,7 @@ dependencies = [
447
  requires-dist = [
448
  { name = "numpy", specifier = ">=2.3.3" },
449
  { name = "snac", specifier = ">=1.2.1" },
 
450
  { name = "torch", specifier = ">=2.8.0" },
451
  { name = "transformers", specifier = ">=4.56.1" },
452
  ]
@@ -460,6 +532,15 @@ wheels = [
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461
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462
 
 
 
 
 
 
 
 
 
 
463
  [[package]]
464
  name = "pyyaml"
465
  version = "6.0.2"
@@ -620,6 +701,25 @@ wheels = [
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621
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623
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624
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625
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515
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521
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725
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