Duplicate from mutisya/kik_asr_demo_1

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README.md

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+---
+title: Kikuyu Asr Demo 1
+emoji: 🌖
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 3.19.1
+app_file: app.py
+pinned: false
+duplicated_from: mutisya/kik_asr_demo_1
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+from pydub import AudioSegment
+import pyaudioconvert as pac
+import torch
+import torchaudio
+import sox
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
+
+
+def convert (audio):
+  file_name = audio
+  if file_name.endswith("mp3") or file_name.endswith("wav") or file_name.endswith("ogg"):
+    if file_name.endswith("mp3"):
+      sound = AudioSegment.from_mp3(file_name)
+      sound.export(audio, format="wav")
+    elif file_name.endswith("ogg"):
+      sound = AudioSegment.from_ogg(audio)
+      sound.export(audio, format="wav")
+  else:
+    return False
+  pac.convert_wav_to_16bit_mono(audio,audio)
+  return True
+
+
+def parse_transcription_with_lm(logits):
+    result = processor_with_LM.batch_decode(logits.cpu().numpy())
+    text = result.text
+    transcription = text[0].replace('<s>','')
+    return transcription
+
+def parse_transcription(logits):
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return transcription
+
+def transcribe(audio, audio_microphone, applyLM):
+    audio_path = audio_microphone if audio_microphone else audio
+    speech_array, sampling_rate = torchaudio.load(audio_path)
+    speech = torchaudio.functional.resample(speech_array, orig_freq=sampling_rate, new_freq=16000).squeeze().numpy()
+    """
+    if convert(audio_path)== False:
+        return "The format must be mp3,wav and ogg"
+    speech, sample_rate = torchaudio.load(audio_path)
+    """
+    
+    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(inputs.input_values).logits
+
+    if applyLM:
+        return parse_transcription_with_lm(logits)
+    else:
+        return parse_transcription(logits)
+
+auth_token = os.environ.get("key") or True
+model_id = "mutisya/wav2vec2-300m-kik-t22-1k-ft-withLM"
+processor = Wav2Vec2Processor.from_pretrained(model_id, use_auth_token=auth_token)
+processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id, use_auth_token=auth_token)
+model = Wav2Vec2ForCTC.from_pretrained(model_id, use_auth_token=auth_token)
+
+
+gradio_ui = gr.Interface(
+    fn=transcribe,
+    title="Kikuyu Speech Recognition",
+    description="",
+    inputs=[gr.Audio(label="Upload Audio File", type="filepath", optional=True), 
+            gr.Audio(source="microphone", type="filepath", optional=True, label="Record from microphone"),
+            gr.Checkbox(label="Apply LM", value=False)],
+    outputs=[gr.outputs.Textbox(label="Recognized speech")]
+)
+gradio_ui.launch(enable_queue=True)

packages.txt

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+libsndfile1
+sox
+ffmpeg

requirements.txt

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+gradio
+https://github.com/kpu/kenlm/archive/master.zip
+pyctcdecode
+soundfile
+torch
+torchaudio
+transformers
+pydub 
+pyaudioconvert
+sox
+scipy