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Running
Joseph Pollack
commited on
fix count output mismatch issue , adds authentication warning , adds authentication fallbacks
Browse files- __pycache__/interface.cpython-313.pyc +0 -0
- interface.py +33 -3
__pycache__/interface.cpython-313.pyc
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Binary files a/__pycache__/interface.cpython-313.pyc and b/__pycache__/interface.cpython-313.pyc differ
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interface.py
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@@ -388,9 +388,17 @@ def load_multilingual_phrases(language="en", max_phrases=None, split="train"):
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try:
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print(f"Loading phrases from NVIDIA Granary dataset for language: {language}")
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# Load Granary dataset with ASR (speech recognition) split
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# Use streaming to handle large datasets efficiently
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phrases = []
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count = 0
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@@ -427,7 +435,12 @@ def load_multilingual_phrases(language="en", max_phrases=None, split="train"):
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raise Exception("No phrases found")
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except Exception as e:
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# Fallback to basic phrases if Granary fails
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print("Using fallback phrases")
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@@ -500,6 +513,23 @@ with gr.Blocks(title="Voxtral ASR Fine-tuning") as demo:
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Read the phrases below and record them. Then start fine-tuning.
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""")
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# Hidden state to track dataset JSONL path
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jsonl_path_state = gr.State("")
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@@ -785,7 +815,7 @@ with gr.Blocks(title="Voxtral ASR Fine-tuning") as demo:
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inputs=[language_selector],
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outputs=[phrase_texts_state, visible_rows_state] + phrase_markdowns + rec_components + [
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add_rows_btn, record_dataset_btn, dataset_status, advanced_accordion,
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save_rec_btn, start_btn, logs_box
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]
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)
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try:
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print(f"Loading phrases from NVIDIA Granary dataset for language: {language}")
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# Check for authentication token
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token = os.getenv("HF_TOKEN") or os.getenv("HF_WRITE_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
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# Load Granary dataset with ASR (speech recognition) split
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# Use streaming to handle large datasets efficiently
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if token:
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print(f"Using authentication token for Granary dataset access")
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ds = load_dataset("nvidia/Granary", granary_lang, split="asr", streaming=True, token=token)
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else:
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print(f"No HF_TOKEN found, attempting to load Granary dataset without authentication")
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ds = load_dataset("nvidia/Granary", granary_lang, split="asr", streaming=True)
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phrases = []
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count = 0
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raise Exception("No phrases found")
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except Exception as e:
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error_msg = str(e).lower()
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if "401" in error_msg or "unauthorized" in error_msg:
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print(f"Granary dataset authentication failed for {language}: {e}")
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print("This dataset requires a Hugging Face token. Please set HF_TOKEN environment variable.")
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else:
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print(f"Granary dataset loading failed for {language}: {e}")
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# Fallback to basic phrases if Granary fails
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print("Using fallback phrases")
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Read the phrases below and record them. Then start fine-tuning.
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""")
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# Check for HF_TOKEN and show warning if missing
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hf_token = os.getenv("HF_TOKEN") or os.getenv("HF_WRITE_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN")
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if not hf_token:
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gr.HTML(
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"""
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<div style="background-color: rgba(245, 158, 11, 0.1); border: 1px solid rgba(245, 158, 11, 0.3); border-radius: 8px; padding: 12px; margin-bottom: 16px;">
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<p style="color: rgb(234, 88, 12); margin: 0; font-size: 14px; font-weight: 600;">
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⚠️ No HF_TOKEN detected
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</p>
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<p style="color: rgb(234, 88, 12); margin: 6px 0 0; font-size: 12px;">
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Set HF_TOKEN environment variable to access NVIDIA Granary dataset with authentic multilingual phrases.
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Currently using fallback phrases for demonstration.
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</p>
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</div>
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"""
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)
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# Hidden state to track dataset JSONL path
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jsonl_path_state = gr.State("")
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inputs=[language_selector],
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outputs=[phrase_texts_state, visible_rows_state] + phrase_markdowns + rec_components + [
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add_rows_btn, record_dataset_btn, dataset_status, advanced_accordion,
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save_rec_btn, push_recordings_btn, start_btn, logs_box
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]
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)
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