Automatic Speech Recognition
Transformers
PyTorch
Turkish
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
Instructions to use cahya/wav2vec2-base-turkish-cv7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/wav2vec2-base-turkish-cv7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/wav2vec2-base-turkish-cv7")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cahya/wav2vec2-base-turkish-cv7") model = AutoModelForCTC.from_pretrained("cahya/wav2vec2-base-turkish-cv7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update the run config
Browse files- config.json +0 -1
- run.sh +4 -4
config.json
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@@ -98,7 +98,6 @@
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.17.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 40,
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1,
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1
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],
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"transformers_version": "4.17.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 40,
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run.sh
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@@ -13,13 +13,13 @@ python run_speech_recognition_ctc.py \
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--per_device_eval_batch_size="8" \
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--gradient_accumulation_steps="4" \
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--learning_rate="3e-4" \
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--warmup_steps="
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--length_column_name="input_length" \
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--evaluation_strategy="steps" \
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--text_column_name="sentence" \
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--save_steps="
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--eval_steps="
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--logging_steps="
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--layerdrop="0.0" \
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--activation_dropout="0.1" \
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--save_total_limit="3" \
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--per_device_eval_batch_size="8" \
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--gradient_accumulation_steps="4" \
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--learning_rate="3e-4" \
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+
--warmup_steps="100" \
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--length_column_name="input_length" \
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--evaluation_strategy="steps" \
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--text_column_name="sentence" \
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--save_steps="200" \
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--eval_steps="200" \
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--logging_steps="200" \
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--layerdrop="0.0" \
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--activation_dropout="0.1" \
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--save_total_limit="3" \
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