freddyaboulton HF staff ylacombe HF staff commited on
Commit
bddd843
1 Parent(s): fcf0aa2

Correct prompt padding side (#1)

Browse files

- Correct prompt padding side (037776cca036c2b340673b03e3f25470c913938e)
- Update app.py (627dc63ff0fb3ceb1448818235c9f532da50a2b7)


Co-authored-by: Yoach Lacombe <[email protected]>

Files changed (1) hide show
  1. app.py +4 -3
app.py CHANGED
@@ -29,7 +29,8 @@ model = ParlerTTSForConditionalGeneration.from_pretrained(
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  client = InferenceClient()
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- tokenizer = AutoTokenizer.from_pretrained(repo_id)
 
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  feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
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  SAMPLE_RATE = feature_extractor.sampling_rate
@@ -87,8 +88,8 @@ def generate_base(subject, setting):
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  gr.Info("Generating Audio")
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  description = "Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality."
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- story_tokens = tokenizer(model_input_tokens, return_tensors="pt", padding=True).input_ids.to(device)
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- description_tokens = tokenizer([description for _ in range(len(model_input_tokens))], return_tensors="pt").input_ids.to(device)
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  speech_output = model.generate(input_ids=description_tokens, prompt_input_ids=story_tokens)
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  speech_output = [output.cpu().numpy() for output in speech_output]
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  gr.Info("Generated Audio")
 
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  client = InferenceClient()
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+ description_tokenizer = AutoTokenizer.from_pretrained(repo_id)
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+ prompt_tokenizer = AutoTokenizer.from_pretrained(repo_id, padding_side="left")
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  feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
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  SAMPLE_RATE = feature_extractor.sampling_rate
 
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  gr.Info("Generating Audio")
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  description = "Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality."
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+ story_tokens = prompt_tokenizer(model_input_tokens, return_tensors="pt", padding=True).input_ids.to(device)
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+ description_tokens = description_tokenizer([description for _ in range(len(model_input_tokens))], return_tensors="pt").input_ids.to(device)
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  speech_output = model.generate(input_ids=description_tokens, prompt_input_ids=story_tokens)
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  speech_output = [output.cpu().numpy() for output in speech_output]
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  gr.Info("Generated Audio")