Update app.py

app.py

@@ -1,130 +1,20 @@
-import gradio as gr
-import json
-import librosa
-import os
-import soundfile as sf
-import tempfile
-import uuid
-
-import torch
-
-from nemo.collections.asr.models import ASRModel
-from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchMultiTaskAED
-from nemo.collections.asr.parts.utils.transcribe_utils import get_buffered_pred_feat_multitaskAED
-
-SAMPLE_RATE = 16000 # Hz
-MAX_AUDIO_MINUTES = 10 # wont try to transcribe if longer than this
-
-model = ASRModel.from_pretrained("nvidia/canary-1b")
-model.eval()
-
-# make sure beam size always 1 for consistency
-model.change_decoding_strategy(None)
-decoding_cfg = model.cfg.decoding
-decoding_cfg.beam.beam_size = 1
-model.change_decoding_strategy(decoding_cfg)
-
-# setup for buffered inference
-model.cfg.preprocessor.dither = 0.0
-model.cfg.preprocessor.pad_to = 0
-
-feature_stride = model.cfg.preprocessor['window_stride']
-model_stride_in_secs = feature_stride * 8 # 8 = model stride, which is 8 for FastConformer
-
-frame_asr = FrameBatchMultiTaskAED(
-	asr_model=model,
-	frame_len=40.0,
-	total_buffer=40.0,
-	batch_size=16,
+from transformers import pipeline
+from gradio import Interface, inputs, outputs
+
+# Initialize the text generation pipeline
+generator = pipeline('text-generation', model='microsoft/Phi-3-mini-4k-instruct-gguf')
+
+def generate_text(prompt):
+    # Generate text
+    output = generator(prompt, max_length=100)
+    return output[0]['generated_text']
+
+# Create a Gradio interface
+iface = Interface(
+    fn=generate_text, 
+    inputs=inputs.Textbox(lines=5, placeholder="Enter your prompt here..."), 
+    outputs=outputs.Textbox()
 )
 
-amp_dtype = torch.float16
-
-
-def convert_audio(audio_filepath, tmpdir, utt_id):
-	"""
-	Convert all files to monochannel 16 kHz wav files.
-	Do not convert and raise error if audio too long.
-	Returns output filename and duration.
-	"""
-
-	data, sr = librosa.load(audio_filepath, sr=None, mono=True)
-
-	duration = librosa.get_duration(y=data, sr=sr)
-
-	if duration / 60.0 > MAX_AUDIO_MINUTES:
-		raise gr.Error(
-			f"This demo can transcribe up to {MAX_AUDIO_MINUTES} minutes of audio. "
-			"If you wish, you may trim the audio using the Audio viewer in Step 1 "
-			"(click on the scissors icon to start trimming audio)."
-		)
-
-	if sr != SAMPLE_RATE:
-		data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
-
-	out_filename = os.path.join(tmpdir, utt_id + '.wav')
-
-	# save output audio
-	sf.write(out_filename, data, SAMPLE_RATE)
-
-	return out_filename, duration
-
-def transcribe(audio_filepath):
-
-	if audio_filepath is None:
-		raise gr.Error("Please provide some input audio: either upload an audio file or use the microphone")
-	
-	utt_id = uuid.uuid4()
-	with tempfile.TemporaryDirectory() as tmpdir:
-		converted_audio_filepath, duration = convert_audio(audio_filepath, tmpdir, str(utt_id))
-        
-		# make manifest file and save
-		manifest_data = {
-			"audio_filepath": converted_audio_filepath,
-			"source_lang": "en",
-			"target_lang": "en",
-			"taskname": "asr",
-			"pnc": "no",
-			"answer": "predict",
-			"duration": str(duration),
-		}
-
-		manifest_filepath = os.path.join(tmpdir, f'{utt_id}.json')
-
-		with open(manifest_filepath, 'w') as fout:
-			line = json.dumps(manifest_data)
-			fout.write(line + '\n')
-
-		# call transcribe, passing in manifest filepath
-		if duration < 40:
-			output_text = model.transcribe(manifest_filepath)[0]
-		else: # do buffered inference
-			with torch.cuda.amp.autocast(dtype=amp_dtype): # TODO: make it work if no cuda
-				with torch.no_grad():
-					hyps = get_buffered_pred_feat_multitaskAED(
-						frame_asr,
-						model.cfg.preprocessor,
-						model_stride_in_secs,
-						model.device,
-						manifest=manifest_filepath,
-						filepaths=None,
-					)
-
-					output_text = hyps[0].text
-
-	return output_text
-
-
-iface = gr.Interface(
-    fn=transcribe, 
-    inputs=gr.Audio(sources="microphone", type="filepath"), 
-    outputs="text")
-
-iface.queue()
-iface.launch()
-
-
-
-
-
-
+# Launch the interface
+iface.launch()